International US Foreign Currency Exchange Rates Predictor

Author: Andrew Tran

Blog Post Inspiration and Objectives

In this blog post, I was hoping to investigate using Machine Learning to make a financial prediction system. Initially, I was hoping to imitate the ones used for stock prediction. However, since there were too many tutorials on this topic, I decided to investigate another global, financial factor that governs trade internationally: international currency rates. In the news recently, I have heard rumors of how the international standard of utilizing the US dollar could be compromised with other global powers such as China and India rise in global dominance. Thus, I focused my efforts on analyzing the US currency conversion rates to other countries. With that said, let’s try to analyze this topic with some Machine Learning:

Data Preprocessing - Cleaning and Analytics

```{python}
# Import needed libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers
from copy import deepcopy
import datetime as dt
plt.style.use("fivethirtyeight")
```

First, we will read and display the initial dataset in our file system for this blog post, downloaded from Kaggle. This dataset contains loads of valuable information such as almost every major world power’s international US currency conversion rate.

```{python}
# Reading and displaying the initial dataset
df = pd.read_csv("datasets/foreign_exchange_rates.csv")
df
```

	Unnamed: 0	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5212	5212	2019-12-25	ND	ND	ND	ND	ND	ND	ND	ND	...	ND	ND	ND	ND	ND	ND	ND	ND	ND	ND
5213	5213	2019-12-26	1.4411	0.9007	1.5002	0.7688	4.0602	1.3124	6.9949	7.788	...	1.354	6.7295	109.67	4.1337	8.8799	9.4108	181.3	0.9808	30.11	30.15
5214	5214	2019-12-27	1.4331	0.8949	1.4919	0.7639	4.0507	1.3073	6.9954	7.7874	...	1.352	6.6829	109.47	4.126	8.8291	9.3405	181.35	0.9741	30.09	30.14
5215	5215	2019-12-30	1.4278	0.8915	1.4846	0.761	4.0152	1.3058	6.9864	7.7857	...	1.3483	6.6589	108.85	4.1053	8.7839	9.3145	181.6	0.9677	30.04	29.94
5216	5216	2019-12-31	1.4225	0.8907	1.4826	0.7536	4.019	1.2962	6.9618	7.7894	...	1.3446	6.6554	108.67	4.0918	8.7823	9.3425	181.3	0.9677	29.91	29.75

5217 rows × 24 columns

For clarity on the constraints and parameters of the working datasets, I went to find high-level exploratory statistics on all of the datasets: shape, information about all of the entries, etc.

```{python}
# Determining the shape of the initial dataset
df.shape
```

(5217, 24)

```{python}
# Getting a sample of the initial dataset through the seeing the first 10 entries
# completely in the dataset
df.head()
```

	Unnamed: 0	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3

5 rows × 24 columns

```{python}
# Figuring out all of the columns (and their names) available for me to use in the dataset
df.columns
```

Index(['Unnamed: 0', 'Time Serie', 'AUSTRALIA - AUSTRALIAN DOLLAR/US$',
       'EURO AREA - EURO/US$', 'NEW ZEALAND - NEW ZELAND DOLLAR/US$',
       'UNITED KINGDOM - UNITED KINGDOM POUND/US$', 'BRAZIL - REAL/US$',
       'CANADA - CANADIAN DOLLAR/US$', 'CHINA - YUAN/US$',
       'HONG KONG - HONG KONG DOLLAR/US$', 'INDIA - INDIAN RUPEE/US$',
       'KOREA - WON/US$', 'MEXICO - MEXICAN PESO/US$',
       'SOUTH AFRICA - RAND/US$', 'SINGAPORE - SINGAPORE DOLLAR/US$',
       'DENMARK - DANISH KRONE/US$', 'JAPAN - YEN/US$',
       'MALAYSIA - RINGGIT/US$', 'NORWAY - NORWEGIAN KRONE/US$',
       'SWEDEN - KRONA/US$', 'SRI LANKA - SRI LANKAN RUPEE/US$',
       'SWITZERLAND - FRANC/US$', 'TAIWAN - NEW TAIWAN DOLLAR/US$',
       'THAILAND - BAHT/US$'],
      dtype='object')

```{python}
# Figuring out the number of duplicated elements in the dataset
# (could be problematic if not resolved)
df.duplicated().sum()
```

0

```{python}
# Figuring out the number of 'null'/'NaN' elements in the dataset 
# (if NaN filling is needed or not)
(df.isnull().sum() / df.shape[0]) * 100
```

Unnamed: 0                                   0.0
Time Serie                                   0.0
AUSTRALIA - AUSTRALIAN DOLLAR/US$            0.0
EURO AREA - EURO/US$                         0.0
NEW ZEALAND - NEW ZELAND DOLLAR/US$          0.0
UNITED KINGDOM - UNITED KINGDOM POUND/US$    0.0
BRAZIL - REAL/US$                            0.0
CANADA - CANADIAN DOLLAR/US$                 0.0
CHINA - YUAN/US$                             0.0
HONG KONG - HONG KONG DOLLAR/US$             0.0
INDIA - INDIAN RUPEE/US$                     0.0
KOREA - WON/US$                              0.0
MEXICO - MEXICAN PESO/US$                    0.0
SOUTH AFRICA - RAND/US$                      0.0
SINGAPORE - SINGAPORE DOLLAR/US$             0.0
DENMARK - DANISH KRONE/US$                   0.0
JAPAN - YEN/US$                              0.0
MALAYSIA - RINGGIT/US$                       0.0
NORWAY - NORWEGIAN KRONE/US$                 0.0
SWEDEN - KRONA/US$                           0.0
SRI LANKA - SRI LANKAN RUPEE/US$             0.0
SWITZERLAND - FRANC/US$                      0.0
TAIWAN - NEW TAIWAN DOLLAR/US$               0.0
THAILAND - BAHT/US$                          0.0
dtype: float64

```{python}
# Getting basic information about the dataset
df.info()
```

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5217 entries, 0 to 5216
Data columns (total 24 columns):
 #   Column                                     Non-Null Count  Dtype 
---  ------                                     --------------  ----- 
 0   Unnamed: 0                                 5217 non-null   int64 
 1   Time Serie                                 5217 non-null   object
 2   AUSTRALIA - AUSTRALIAN DOLLAR/US$          5217 non-null   object
 3   EURO AREA - EURO/US$                       5217 non-null   object
 4   NEW ZEALAND - NEW ZELAND DOLLAR/US$        5217 non-null   object
 5   UNITED KINGDOM - UNITED KINGDOM POUND/US$  5217 non-null   object
 6   BRAZIL - REAL/US$                          5217 non-null   object
 7   CANADA - CANADIAN DOLLAR/US$               5217 non-null   object
 8   CHINA - YUAN/US$                           5217 non-null   object
 9   HONG KONG - HONG KONG DOLLAR/US$           5217 non-null   object
 10  INDIA - INDIAN RUPEE/US$                   5217 non-null   object
 11  KOREA - WON/US$                            5217 non-null   object
 12  MEXICO - MEXICAN PESO/US$                  5217 non-null   object
 13  SOUTH AFRICA - RAND/US$                    5217 non-null   object
 14  SINGAPORE - SINGAPORE DOLLAR/US$           5217 non-null   object
 15  DENMARK - DANISH KRONE/US$                 5217 non-null   object
 16  JAPAN - YEN/US$                            5217 non-null   object
 17  MALAYSIA - RINGGIT/US$                     5217 non-null   object
 18  NORWAY - NORWEGIAN KRONE/US$               5217 non-null   object
 19  SWEDEN - KRONA/US$                         5217 non-null   object
 20  SRI LANKA - SRI LANKAN RUPEE/US$           5217 non-null   object
 21  SWITZERLAND - FRANC/US$                    5217 non-null   object
 22  TAIWAN - NEW TAIWAN DOLLAR/US$             5217 non-null   object
 23  THAILAND - BAHT/US$                        5217 non-null   object
dtypes: int64(1), object(23)
memory usage: 978.3+ KB

Additionally, before handing my combined Book dataset over for Machine Learning training and prediction, I need to clean the data prior to the analysis stage: removing duplicates, deleting null/NaN values, fixing types of columns, filling invalid values with suitable alternatives, etc.

```{python}
# Removing unnecessary/unnamed columns in the dataset
df = df.drop("Unnamed: 0", axis=1)
df
```

	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	INDIA - INDIAN RUPEE/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	43.55	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	43.55	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	43.55	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	43.55	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	43.55	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5212	2019-12-25	ND	ND	ND	ND	ND	ND	ND	ND	ND	...	ND	ND	ND	ND	ND	ND	ND	ND	ND	ND
5213	2019-12-26	1.4411	0.9007	1.5002	0.7688	4.0602	1.3124	6.9949	7.788	71.28	...	1.354	6.7295	109.67	4.1337	8.8799	9.4108	181.3	0.9808	30.11	30.15
5214	2019-12-27	1.4331	0.8949	1.4919	0.7639	4.0507	1.3073	6.9954	7.7874	71.45	...	1.352	6.6829	109.47	4.126	8.8291	9.3405	181.35	0.9741	30.09	30.14
5215	2019-12-30	1.4278	0.8915	1.4846	0.761	4.0152	1.3058	6.9864	7.7857	71.3	...	1.3483	6.6589	108.85	4.1053	8.7839	9.3145	181.6	0.9677	30.04	29.94
5216	2019-12-31	1.4225	0.8907	1.4826	0.7536	4.019	1.2962	6.9618	7.7894	71.36	...	1.3446	6.6554	108.67	4.0918	8.7823	9.3425	181.3	0.9677	29.91	29.75

5217 rows × 23 columns

```{python}
# Converting the date column into a string representation to a parsable
# Datetime object (needed for later)
df = df.rename(columns={"Time Serie": "DATE"})

def str_to_datetime(date_str: str):
    split = tuple(date_str.split("-"))
    year, month, day = int(split[0]), int(split[1]), int(split[2])
    return dt.datetime(year, month, day)

df["DATE"] = df["DATE"].apply(str_to_datetime)
df["DATE"]
```

0      2000-01-03
1      2000-01-04
2      2000-01-05
3      2000-01-06
4      2000-01-07
          ...    
5212   2019-12-25
5213   2019-12-26
5214   2019-12-27
5215   2019-12-30
5216   2019-12-31
Name: DATE, Length: 5217, dtype: datetime64[ns]

As shown below, I had decided to filter out all of the countries except Austrailia, Canada, the United Kingdom, and Switzerland because those countries had the most stable and consistent international currency rates as well as the most comparable (1:1) currency rates with the United States. One additional reason that these countries were selected for this blog post was do the geographical diversity covered most landmass continents across the world, representative of the global market interactions that the United States deals with on a day-to-day basis.

```{python}
# Making the "Date" column the new index (better identifer/key in dataset)
# Taking out other countries except Austrailia, Canada, UK, and 
# Switzerland from dataset for Machine Learning blog post
df.index = df.pop("DATE")
df = df[["AUSTRALIA - AUSTRALIAN DOLLAR/US$", 
         "CANADA - CANADIAN DOLLAR/US$",
         "UNITED KINGDOM - UNITED KINGDOM POUND/US$",
         "SWITZERLAND - FRANC/US$"]]
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.5172	1.4465	0.6146	1.5808
2000-01-04	1.5239	1.4518	0.6109	1.5565
2000-01-05	1.5267	1.4518	0.6092	1.5526
2000-01-06	1.5291	1.4571	0.607	1.554
2000-01-07	1.5272	1.4505	0.6104	1.5623
...	...	...	...	...
2019-12-25	ND	ND	ND	ND
2019-12-26	1.4411	1.3124	0.7688	0.9808
2019-12-27	1.4331	1.3073	0.7639	0.9741
2019-12-30	1.4278	1.3058	0.761	0.9677
2019-12-31	1.4225	1.2962	0.7536	0.9677

5217 rows × 4 columns

```{python}
# Converting of all numerical international currency rates to 2-decimal 
# rates (easier to work with for later)
for col in df:
    df[col] = df[col].map(lambda entry: entry if entry == "ND" else round(float(entry), 2))
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.52	1.45	0.61	1.58
2000-01-04	1.52	1.45	0.61	1.56
2000-01-05	1.53	1.45	0.61	1.55
2000-01-06	1.53	1.46	0.61	1.55
2000-01-07	1.53	1.45	0.61	1.56
...	...	...	...	...
2019-12-25	ND	ND	ND	ND
2019-12-26	1.44	1.31	0.77	0.98
2019-12-27	1.43	1.31	0.76	0.97
2019-12-30	1.43	1.31	0.76	0.97
2019-12-31	1.42	1.3	0.75	0.97

5217 rows × 4 columns

```{python}
# Removing all of the dated rows that contains "ND" as a way to avoid problems 
# when trying to interlay Machine Learning algorithms that require only
# quantitative data with categorical entries
nd_index_list: set[str] = set()
for row_index, _ in df.iterrows():
    for col_entry in df.loc[row_index]:
        if col_entry == "ND":
             nd_index_list.add(row_index)

df = df.drop(labels=list(nd_index_list), axis=0)
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.52	1.45	0.61	1.58
2000-01-04	1.52	1.45	0.61	1.56
2000-01-05	1.53	1.45	0.61	1.55
2000-01-06	1.53	1.46	0.61	1.55
2000-01-07	1.53	1.45	0.61	1.56
...	...	...	...	...
2019-12-24	1.44	1.32	0.77	0.98
2019-12-26	1.44	1.31	0.77	0.98
2019-12-27	1.43	1.31	0.76	0.97
2019-12-30	1.43	1.31	0.76	0.97
2019-12-31	1.42	1.3	0.75	0.97

5019 rows × 4 columns

As shown in the code snippet below, with some online references, the df_to_windowed_df function describes a way to use prediction values. A new international currency rate dataframe is created which includes the rows encompassing the 3 previous dates prior to the current date of when the international currency rate was reported as well as the current rate that day. This would be helpful for later Machine Learning predictions because the previous 3 dates and the current will provide it enough context to make more insightful predictions on the future of the international currency rates between that respective country specified and the United States. Later, as you will see, the 3 previous days are going to be the input and the current currency rate (Target) will be our output.

```{python}
def df_to_windowed_df(data_frame: pd.DataFrame, first_date_str: str, last_date_str: str, n: int, col_name: str):
    first_date = str_to_datetime(first_date_str)
    last_date = str_to_datetime(last_date_str)
    
    target_date = first_date
    
    dates: list = []
    X: list = []
    Y: list = []
    
    last_time = False
    while True:
        df_subset = data_frame.loc[:target_date].tail(n + 1)
        
        if (len(df_subset) != n + 1):
            print("Error: Window of size " + str(n) + " is too large for date " + str(target_date))
            return
        
        values = df_subset[col_name].to_numpy()
        x, y = values[:-1], values[-1]
        
        dates.append(target_date)
        X.append(x)
        Y.append(y)
        
        next_week = data_frame.loc[target_date:target_date + dt.timedelta(days=7)]
        next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
        next_date_str = next_datetime_str.split("T")[0]
        year_month_day = next_date_str.split("-")
        year, month, day = year_month_day
        next_date = dt.datetime(year=int(year), month=int(month), day=int(day))
        
        if last_time:
            break
        
        target_date = next_date
        if (target_date == last_date):
            last_time = True
    
    returned_df = pd.DataFrame({})
    returned_df["Target Date"] = dates
    
    X = np.array(X)
    for i in range(0, n):
        X[:, i]
        returned_df["Target-" + str(n - i)] = X[:, i]
    returned_df["Target"] = Y
    
    return returned_df 
```

Thus, with the df_to_windowed_df function created, we created one for every country to later use for Machine Learning model training and eventual predictions of future rates: Austrailia, Canada, the United Kingdom, and Switzerland.

```{python}
# Convert Austrailia's dataframe to a windowed dataframe
windowed_df_austrailia = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="AUSTRALIA - AUSTRALIAN DOLLAR/US$")
windowed_df_austrailia  
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.52	1.52	1.53	1.53
1	2000-01-07	1.52	1.53	1.53	1.53
2	2000-01-10	1.53	1.53	1.53	1.52
3	2000-01-11	1.53	1.53	1.52	1.52
4	2000-01-12	1.53	1.52	1.52	1.52
...	...	...	...	...	...
5011	2019-12-24	1.45	1.45	1.45	1.44
5012	2019-12-26	1.45	1.45	1.44	1.44
5013	2019-12-27	1.45	1.44	1.44	1.43
5014	2019-12-30	1.44	1.44	1.43	1.43
5015	2019-12-31	1.44	1.43	1.43	1.42

5016 rows × 5 columns

```{python}
# Convert Canada's dataframe to a windowed dataframe
windowed_df_canada = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="CANADA - CANADIAN DOLLAR/US$")
windowed_df_canada
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.45	1.45	1.45	1.46
1	2000-01-07	1.45	1.45	1.46	1.45
2	2000-01-10	1.45	1.46	1.45	1.46
3	2000-01-11	1.46	1.45	1.46	1.46
4	2000-01-12	1.45	1.46	1.46	1.46
...	...	...	...	...	...
5011	2019-12-24	1.31	1.32	1.32	1.32
5012	2019-12-26	1.32	1.32	1.32	1.31
5013	2019-12-27	1.32	1.32	1.31	1.31
5014	2019-12-30	1.32	1.31	1.31	1.31
5015	2019-12-31	1.31	1.31	1.31	1.30

5016 rows × 5 columns

```{python}
# Convert United Kingdom's dataframe to a windowed dataframe
windowed_df_united_kingdom = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="UNITED KINGDOM - UNITED KINGDOM POUND/US$")
windowed_df_united_kingdom
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	0.61	0.61	0.61	0.61
1	2000-01-07	0.61	0.61	0.61	0.61
2	2000-01-10	0.61	0.61	0.61	0.61
3	2000-01-11	0.61	0.61	0.61	0.61
4	2000-01-12	0.61	0.61	0.61	0.61
...	...	...	...	...	...
5011	2019-12-24	0.77	0.77	0.77	0.77
5012	2019-12-26	0.77	0.77	0.77	0.77
5013	2019-12-27	0.77	0.77	0.77	0.76
5014	2019-12-30	0.77	0.77	0.76	0.76
5015	2019-12-31	0.77	0.76	0.76	0.75

5016 rows × 5 columns

```{python}
# Convert Switzerland's dataframe to a windowed dataframe
windowed_df_switzerland = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="SWITZERLAND - FRANC/US$")
windowed_df_switzerland
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.58	1.56	1.55	1.55
1	2000-01-07	1.56	1.55	1.55	1.56
2	2000-01-10	1.55	1.55	1.56	1.57
3	2000-01-11	1.55	1.56	1.57	1.56
4	2000-01-12	1.56	1.57	1.56	1.57
...	...	...	...	...	...
5011	2019-12-24	0.98	0.98	0.98	0.98
5012	2019-12-26	0.98	0.98	0.98	0.98
5013	2019-12-27	0.98	0.98	0.98	0.97
5014	2019-12-30	0.98	0.98	0.97	0.97
5015	2019-12-31	0.98	0.97	0.97	0.97

5016 rows × 5 columns

As the Machine Learning model that will be used later in this blog post only accepts numpy arrays, we need to extract each of the following quantities from each respective country’s dataframe: the dates as an np.array, the X-values for input as an 3-dimensional np.array, and the Y-values for output as an np.array. Thus, with some online references, our codebase now has a function called windowed_df_to_date_X_y as shown below to account for this needed transformation.

```{python}
def windowed_df_to_date_X_y(windowed_dataframe: pd.DataFrame):
    df_as_np = windowed_dataframe.to_numpy()
    
    dates = df_as_np[:, 0]
    
    middle_matrix = df_as_np[:, 1:-1]
    X = middle_matrix.reshape((len(dates), middle_matrix.shape[1], 1))
    Y = df_as_np[:, -1]
    
    return dates, X.astype(np.float32), Y.astype(np.float32)

dates_austrailia, X_austrailia, y_austrailia = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_austrailia)
dates_canada, X_canada, y_canada = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_canada)
dates_united_kingdom, X_united_kingdom, y_united_kingdom = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_united_kingdom)
dates_switzerland, X_switzerland, y_switzerland = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_switzerland)

print(dates_austrailia.shape, X_austrailia.shape, y_austrailia.shape)
print(dates_canada.shape, X_canada.shape, y_canada.shape)
print(dates_united_kingdom.shape, X_united_kingdom.shape, y_united_kingdom.shape)
print(dates_switzerland.shape, X_switzerland.shape, y_switzerland.shape)
len(windowed_df_austrailia), len(windowed_df_canada), len(windowed_df_united_kingdom), len(windowed_df_switzerland)
```

(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)

(5016, 5016, 5016, 5016)

Here, I am trying to a visualization of the cleaned dataset before we pass it over for Machine Learning training and prediction. To show the rate of change for the International Currency Rates for Austrailia, Canada, the United Kingdom, and Switzerland over the 20-year period as collected in the data set, I put together a line graph as shown below.

```{python}
# Plotting the each four countries currency rates (per US dollar) from 
# 2000 - 2020
plt.plot(df.index, df["AUSTRALIA - AUSTRALIAN DOLLAR/US$"], label="AUS to USD", linewidth=1)
plt.plot(df.index, df[ "CANADA - CANADIAN DOLLAR/US$"], label="CAN to USD", linewidth=1)
plt.plot(df.index, df["UNITED KINGDOM - UNITED KINGDOM POUND/US$"], label="GBR to USD", linewidth=1)
plt.plot(df.index, df["SWITZERLAND - FRANC/US$"], label="CHE to USD", linewidth=1)

plt.legend(loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date", fontsize=12)
plt.ylabel("Currency Rate of the Respective Country to USD", fontsize=12)
plt.title("Culmination of the All Four Countries' Datasets - After Parsing But Before Machine Learning Alterations")
plt.show()
```

Machine Learning - Model Training and Evaluation

Great, now we are onto the Machine Learning part of the blog post!

For this blog post, I worked to create Machine Learning models for each country’s dataset separately. I started with Austrailia’s data first (as shown below):

```{python}
# Getting the location of the 80th and 90th percentile of the number of points 
# in the dataset for later train, vailidation, and test category splitting
percentile_80 = int(len(windowed_df_austrailia) * 0.8)
percentile_90 = int(len(windowed_df_austrailia) * 0.9)
percentile_80, percentile_90
```

(4012, 4514)

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Austrailia’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively. This split should be acceptable because I did not mix-and-match training & validation datasets with my testing datasets, wihch would invalidate my entire Machine Learning model. Additionally, looking at the presented in the validation and testing datasets, the range of these respective datasets have been reached before in my training dataset, meaning I anticipated my model should be able to reasonably predict these expected ranges with considerable accuracy.

```{python}
# Splitting Austrailia's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_austrailia_train, X_austrailia_train, y_austrailia_train = dates_austrailia[:percentile_80], X_austrailia[:percentile_80], y_austrailia[:percentile_80]
dates_austrailia_val, X_austrailia_val, y_austrailia_val = dates_austrailia[percentile_80:percentile_90], X_austrailia[percentile_80:percentile_90], y_austrailia[percentile_80:percentile_90]
dates_austrailia_test, X_austrailia_test, y_austrailia_test = dates_austrailia[percentile_90:], X_austrailia[percentile_90:], y_austrailia[percentile_90:]

plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Austrailia's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Austrailia
austrailia_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

austrailia_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

austrailia_model.fit(X_austrailia_train, y_austrailia_train, validation_data=(X_austrailia_val, y_austrailia_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:46 - loss: 1.9534 - mean_absolute_error: 1.3618 24/126 [====>.........................] - ETA: 0s - loss: 1.2803 - mean_absolute_error: 1.0872   50/126 [==========>...................] - ETA: 0s - loss: 0.6821 - mean_absolute_error: 0.6717 72/126 [================>.............] - ETA: 0s - loss: 0.4771 - mean_absolute_error: 0.4906 98/126 [======================>.......] - ETA: 0s - loss: 0.3520 - mean_absolute_error: 0.3774125/126 [============================>.] - ETA: 0s - loss: 0.2769 - mean_absolute_error: 0.3077126/126 [==============================] - 3s 6ms/step - loss: 0.2761 - mean_absolute_error: 0.3070 - val_loss: 2.4687e-04 - val_mean_absolute_error: 0.0131
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0029 - mean_absolute_error: 0.0435 26/126 [=====>........................] - ETA: 0s - loss: 0.0030 - mean_absolute_error: 0.0457 53/126 [===========>..................] - ETA: 0s - loss: 0.0028 - mean_absolute_error: 0.0436 80/126 [==================>...........] - ETA: 0s - loss: 0.0024 - mean_absolute_error: 0.0409106/126 [========================>.....] - ETA: 0s - loss: 0.0022 - mean_absolute_error: 0.0382126/126 [==============================] - 0s 2ms/step - loss: 0.0020 - mean_absolute_error: 0.0362 - val_loss: 1.2844e-04 - val_mean_absolute_error: 0.0089
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 4.3855e-04 - mean_absolute_error: 0.0164 27/126 [=====>........................] - ETA: 0s - loss: 6.8452e-04 - mean_absolute_error: 0.0212 54/126 [===========>..................] - ETA: 0s - loss: 6.2709e-04 - mean_absolute_error: 0.0201 80/126 [==================>...........] - ETA: 0s - loss: 5.6674e-04 - mean_absolute_error: 0.0189107/126 [========================>.....] - ETA: 0s - loss: 5.0701e-04 - mean_absolute_error: 0.0176126/126 [==============================] - 0s 2ms/step - loss: 4.7762e-04 - mean_absolute_error: 0.0171 - val_loss: 1.2990e-04 - val_mean_absolute_error: 0.0088
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 1.5570e-04 - mean_absolute_error: 0.0099 27/126 [=====>........................] - ETA: 0s - loss: 2.6991e-04 - mean_absolute_error: 0.0122 54/126 [===========>..................] - ETA: 0s - loss: 2.7925e-04 - mean_absolute_error: 0.0125 81/126 [==================>...........] - ETA: 0s - loss: 2.7030e-04 - mean_absolute_error: 0.0124107/126 [========================>.....] - ETA: 0s - loss: 2.6547e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.7650e-04 - mean_absolute_error: 0.0122 - val_loss: 1.2514e-04 - val_mean_absolute_error: 0.0087
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 2.5293e-04 - mean_absolute_error: 0.0123 27/126 [=====>........................] - ETA: 0s - loss: 3.0636e-04 - mean_absolute_error: 0.0121 53/126 [===========>..................] - ETA: 0s - loss: 2.6883e-04 - mean_absolute_error: 0.0118 80/126 [==================>...........] - ETA: 0s - loss: 2.7372e-04 - mean_absolute_error: 0.0119107/126 [========================>.....] - ETA: 0s - loss: 2.6632e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6389e-04 - mean_absolute_error: 0.0118 - val_loss: 1.2755e-04 - val_mean_absolute_error: 0.0088
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 2.8138e-04 - mean_absolute_error: 0.0138 27/126 [=====>........................] - ETA: 0s - loss: 3.0079e-04 - mean_absolute_error: 0.0122 54/126 [===========>..................] - ETA: 0s - loss: 2.8541e-04 - mean_absolute_error: 0.0119 80/126 [==================>...........] - ETA: 0s - loss: 2.7392e-04 - mean_absolute_error: 0.0118106/126 [========================>.....] - ETA: 0s - loss: 2.6978e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6494e-04 - mean_absolute_error: 0.0118 - val_loss: 1.8028e-04 - val_mean_absolute_error: 0.0106
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 2.4858e-04 - mean_absolute_error: 0.0111 27/126 [=====>........................] - ETA: 0s - loss: 2.2459e-04 - mean_absolute_error: 0.0114 53/126 [===========>..................] - ETA: 0s - loss: 2.4694e-04 - mean_absolute_error: 0.0116 79/126 [=================>............] - ETA: 0s - loss: 2.4778e-04 - mean_absolute_error: 0.0116105/126 [========================>.....] - ETA: 0s - loss: 2.5522e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.6391e-04 - mean_absolute_error: 0.0118 - val_loss: 1.4271e-04 - val_mean_absolute_error: 0.0093
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 3.5699e-04 - mean_absolute_error: 0.0142 27/126 [=====>........................] - ETA: 0s - loss: 2.6262e-04 - mean_absolute_error: 0.0117 52/126 [===========>..................] - ETA: 0s - loss: 2.6066e-04 - mean_absolute_error: 0.0117 76/126 [=================>............] - ETA: 0s - loss: 2.5901e-04 - mean_absolute_error: 0.0117102/126 [=======================>......] - ETA: 0s - loss: 2.6962e-04 - mean_absolute_error: 0.0117126/126 [==============================] - ETA: 0s - loss: 2.6680e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6680e-04 - mean_absolute_error: 0.0118 - val_loss: 1.3617e-04 - val_mean_absolute_error: 0.0090
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 1.8544e-04 - mean_absolute_error: 0.0117 28/126 [=====>........................] - ETA: 0s - loss: 2.9086e-04 - mean_absolute_error: 0.0117 54/126 [===========>..................] - ETA: 0s - loss: 2.7313e-04 - mean_absolute_error: 0.0117 80/126 [==================>...........] - ETA: 0s - loss: 2.7010e-04 - mean_absolute_error: 0.0117106/126 [========================>.....] - ETA: 0s - loss: 2.6468e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6526e-04 - mean_absolute_error: 0.0118 - val_loss: 1.4674e-04 - val_mean_absolute_error: 0.0094
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 2.5334e-04 - mean_absolute_error: 0.0126 26/126 [=====>........................] - ETA: 0s - loss: 2.6108e-04 - mean_absolute_error: 0.0121 52/126 [===========>..................] - ETA: 0s - loss: 2.6341e-04 - mean_absolute_error: 0.0117 77/126 [=================>............] - ETA: 0s - loss: 2.7132e-04 - mean_absolute_error: 0.0119103/126 [=======================>......] - ETA: 0s - loss: 2.6609e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.7317e-04 - mean_absolute_error: 0.0119 - val_loss: 1.9020e-04 - val_mean_absolute_error: 0.0109
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 3.2084e-04 - mean_absolute_error: 0.0120 27/126 [=====>........................] - ETA: 0s - loss: 2.5086e-04 - mean_absolute_error: 0.0119 52/126 [===========>..................] - ETA: 0s - loss: 2.5208e-04 - mean_absolute_error: 0.0118 79/126 [=================>............] - ETA: 0s - loss: 2.5757e-04 - mean_absolute_error: 0.0116105/126 [========================>.....] - ETA: 0s - loss: 2.6448e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6546e-04 - mean_absolute_error: 0.0118 - val_loss: 1.5060e-04 - val_mean_absolute_error: 0.0096
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 2.4727e-04 - mean_absolute_error: 0.0120 27/126 [=====>........................] - ETA: 0s - loss: 2.6099e-04 - mean_absolute_error: 0.0119 53/126 [===========>..................] - ETA: 0s - loss: 2.7070e-04 - mean_absolute_error: 0.0120 79/126 [=================>............] - ETA: 0s - loss: 2.8033e-04 - mean_absolute_error: 0.0122105/126 [========================>.....] - ETA: 0s - loss: 2.7772e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7289e-04 - mean_absolute_error: 0.0120 - val_loss: 1.2530e-04 - val_mean_absolute_error: 0.0087
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 2.1601e-04 - mean_absolute_error: 0.0127 27/126 [=====>........................] - ETA: 0s - loss: 2.4929e-04 - mean_absolute_error: 0.0115 53/126 [===========>..................] - ETA: 0s - loss: 2.5956e-04 - mean_absolute_error: 0.0116 80/126 [==================>...........] - ETA: 0s - loss: 2.7079e-04 - mean_absolute_error: 0.0117107/126 [========================>.....] - ETA: 0s - loss: 2.6568e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6843e-04 - mean_absolute_error: 0.0118 - val_loss: 1.3278e-04 - val_mean_absolute_error: 0.0089
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 4.6261e-04 - mean_absolute_error: 0.0154 27/126 [=====>........................] - ETA: 0s - loss: 2.4801e-04 - mean_absolute_error: 0.0109 53/126 [===========>..................] - ETA: 0s - loss: 2.5672e-04 - mean_absolute_error: 0.0113 80/126 [==================>...........] - ETA: 0s - loss: 2.6201e-04 - mean_absolute_error: 0.0116108/126 [========================>.....] - ETA: 0s - loss: 2.5200e-04 - mean_absolute_error: 0.0115126/126 [==============================] - 0s 2ms/step - loss: 2.5989e-04 - mean_absolute_error: 0.0117 - val_loss: 1.2807e-04 - val_mean_absolute_error: 0.0090
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 2.4521e-04 - mean_absolute_error: 0.0128 28/126 [=====>........................] - ETA: 0s - loss: 2.4231e-04 - mean_absolute_error: 0.0114 54/126 [===========>..................] - ETA: 0s - loss: 2.6426e-04 - mean_absolute_error: 0.0115 80/126 [==================>...........] - ETA: 0s - loss: 2.6583e-04 - mean_absolute_error: 0.0117106/126 [========================>.....] - ETA: 0s - loss: 2.6652e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6158e-04 - mean_absolute_error: 0.0117 - val_loss: 1.3646e-04 - val_mean_absolute_error: 0.0091
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 2.2392e-04 - mean_absolute_error: 0.0121 28/126 [=====>........................] - ETA: 0s - loss: 2.2086e-04 - mean_absolute_error: 0.0113 55/126 [============>.................] - ETA: 0s - loss: 2.5910e-04 - mean_absolute_error: 0.0119 82/126 [==================>...........] - ETA: 0s - loss: 2.7350e-04 - mean_absolute_error: 0.0122108/126 [========================>.....] - ETA: 0s - loss: 2.8742e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.8025e-04 - mean_absolute_error: 0.0122 - val_loss: 1.2830e-04 - val_mean_absolute_error: 0.0090
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 2.7430e-04 - mean_absolute_error: 0.0121 28/126 [=====>........................] - ETA: 0s - loss: 2.4482e-04 - mean_absolute_error: 0.0115 55/126 [============>.................] - ETA: 0s - loss: 2.5735e-04 - mean_absolute_error: 0.0115 81/126 [==================>...........] - ETA: 0s - loss: 2.7038e-04 - mean_absolute_error: 0.0119108/126 [========================>.....] - ETA: 0s - loss: 2.8083e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.8923e-04 - mean_absolute_error: 0.0124 - val_loss: 1.8436e-04 - val_mean_absolute_error: 0.0107
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 2.9754e-04 - mean_absolute_error: 0.0142 28/126 [=====>........................] - ETA: 0s - loss: 2.3309e-04 - mean_absolute_error: 0.0112 55/126 [============>.................] - ETA: 0s - loss: 2.4916e-04 - mean_absolute_error: 0.0115 81/126 [==================>...........] - ETA: 0s - loss: 2.5616e-04 - mean_absolute_error: 0.0117107/126 [========================>.....] - ETA: 0s - loss: 2.7020e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6827e-04 - mean_absolute_error: 0.0119 - val_loss: 1.6959e-04 - val_mean_absolute_error: 0.0106
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 3.6118e-04 - mean_absolute_error: 0.0150 27/126 [=====>........................] - ETA: 0s - loss: 2.7672e-04 - mean_absolute_error: 0.0123 52/126 [===========>..................] - ETA: 0s - loss: 2.7140e-04 - mean_absolute_error: 0.0121 79/126 [=================>............] - ETA: 0s - loss: 2.8022e-04 - mean_absolute_error: 0.0122105/126 [========================>.....] - ETA: 0s - loss: 2.7650e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7183e-04 - mean_absolute_error: 0.0120 - val_loss: 1.4087e-04 - val_mean_absolute_error: 0.0092
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 1.2470e-04 - mean_absolute_error: 0.0084 28/126 [=====>........................] - ETA: 0s - loss: 2.5131e-04 - mean_absolute_error: 0.0114 54/126 [===========>..................] - ETA: 0s - loss: 2.4654e-04 - mean_absolute_error: 0.0114 80/126 [==================>...........] - ETA: 0s - loss: 2.5500e-04 - mean_absolute_error: 0.0115106/126 [========================>.....] - ETA: 0s - loss: 2.5788e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6226e-04 - mean_absolute_error: 0.0117 - val_loss: 1.8263e-04 - val_mean_absolute_error: 0.0106
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 2.2847e-04 - mean_absolute_error: 0.0109 28/126 [=====>........................] - ETA: 0s - loss: 2.4710e-04 - mean_absolute_error: 0.0114 54/126 [===========>..................] - ETA: 0s - loss: 2.4573e-04 - mean_absolute_error: 0.0114 81/126 [==================>...........] - ETA: 0s - loss: 2.4465e-04 - mean_absolute_error: 0.0114108/126 [========================>.....] - ETA: 0s - loss: 2.6156e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.6515e-04 - mean_absolute_error: 0.0118 - val_loss: 2.4263e-04 - val_mean_absolute_error: 0.0126
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 2.9587e-04 - mean_absolute_error: 0.0132 27/126 [=====>........................] - ETA: 0s - loss: 2.5776e-04 - mean_absolute_error: 0.0117 53/126 [===========>..................] - ETA: 0s - loss: 2.4320e-04 - mean_absolute_error: 0.0115 78/126 [=================>............] - ETA: 0s - loss: 2.5909e-04 - mean_absolute_error: 0.0118104/126 [=======================>......] - ETA: 0s - loss: 2.7242e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.7062e-04 - mean_absolute_error: 0.0119 - val_loss: 1.2707e-04 - val_mean_absolute_error: 0.0088
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 9.6141e-04 - mean_absolute_error: 0.0176 28/126 [=====>........................] - ETA: 0s - loss: 2.6614e-04 - mean_absolute_error: 0.0116 55/126 [============>.................] - ETA: 0s - loss: 2.5604e-04 - mean_absolute_error: 0.0116 82/126 [==================>...........] - ETA: 0s - loss: 2.6195e-04 - mean_absolute_error: 0.0117108/126 [========================>.....] - ETA: 0s - loss: 2.5520e-04 - mean_absolute_error: 0.0115126/126 [==============================] - 0s 2ms/step - loss: 2.6058e-04 - mean_absolute_error: 0.0116 - val_loss: 2.0652e-04 - val_mean_absolute_error: 0.0115
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 2.3628e-04 - mean_absolute_error: 0.0111 28/126 [=====>........................] - ETA: 0s - loss: 2.5810e-04 - mean_absolute_error: 0.0114 55/126 [============>.................] - ETA: 0s - loss: 2.5195e-04 - mean_absolute_error: 0.0113 81/126 [==================>...........] - ETA: 0s - loss: 2.4946e-04 - mean_absolute_error: 0.0114108/126 [========================>.....] - ETA: 0s - loss: 2.6068e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6117e-04 - mean_absolute_error: 0.0116 - val_loss: 1.3154e-04 - val_mean_absolute_error: 0.0092
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 1.2968e-04 - mean_absolute_error: 0.0092 28/126 [=====>........................] - ETA: 0s - loss: 3.0398e-04 - mean_absolute_error: 0.0124 54/126 [===========>..................] - ETA: 0s - loss: 2.7050e-04 - mean_absolute_error: 0.0119 81/126 [==================>...........] - ETA: 0s - loss: 2.8544e-04 - mean_absolute_error: 0.0121107/126 [========================>.....] - ETA: 0s - loss: 2.7054e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.7055e-04 - mean_absolute_error: 0.0119 - val_loss: 1.3143e-04 - val_mean_absolute_error: 0.0092
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 8.0506e-04 - mean_absolute_error: 0.0176 25/126 [====>.........................] - ETA: 0s - loss: 3.1216e-04 - mean_absolute_error: 0.0124 50/126 [==========>...................] - ETA: 0s - loss: 2.7294e-04 - mean_absolute_error: 0.0119 76/126 [=================>............] - ETA: 0s - loss: 2.9819e-04 - mean_absolute_error: 0.0123101/126 [=======================>......] - ETA: 0s - loss: 2.7733e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6718e-04 - mean_absolute_error: 0.0118 - val_loss: 1.4351e-04 - val_mean_absolute_error: 0.0093
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 2.5865e-04 - mean_absolute_error: 0.0117 27/126 [=====>........................] - ETA: 0s - loss: 3.0481e-04 - mean_absolute_error: 0.0119 53/126 [===========>..................] - ETA: 0s - loss: 3.1255e-04 - mean_absolute_error: 0.0124 80/126 [==================>...........] - ETA: 0s - loss: 2.9900e-04 - mean_absolute_error: 0.0125107/126 [========================>.....] - ETA: 0s - loss: 2.9954e-04 - mean_absolute_error: 0.0125126/126 [==============================] - 0s 2ms/step - loss: 2.8743e-04 - mean_absolute_error: 0.0124 - val_loss: 1.2433e-04 - val_mean_absolute_error: 0.0087
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 3.0354e-04 - mean_absolute_error: 0.0138 27/126 [=====>........................] - ETA: 0s - loss: 2.4306e-04 - mean_absolute_error: 0.0114 52/126 [===========>..................] - ETA: 0s - loss: 2.6633e-04 - mean_absolute_error: 0.0120 78/126 [=================>............] - ETA: 0s - loss: 2.6902e-04 - mean_absolute_error: 0.0119105/126 [========================>.....] - ETA: 0s - loss: 2.7226e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7438e-04 - mean_absolute_error: 0.0120 - val_loss: 1.3953e-04 - val_mean_absolute_error: 0.0092
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 1.8012e-04 - mean_absolute_error: 0.0102 27/126 [=====>........................] - ETA: 0s - loss: 3.2332e-04 - mean_absolute_error: 0.0128 53/126 [===========>..................] - ETA: 0s - loss: 3.5858e-04 - mean_absolute_error: 0.0144 79/126 [=================>............] - ETA: 0s - loss: 3.2921e-04 - mean_absolute_error: 0.0135104/126 [=======================>......] - ETA: 0s - loss: 3.0585e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 3.1712e-04 - mean_absolute_error: 0.0130 - val_loss: 1.5870e-04 - val_mean_absolute_error: 0.0102
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 2.0840e-04 - mean_absolute_error: 0.0121 27/126 [=====>........................] - ETA: 0s - loss: 3.0117e-04 - mean_absolute_error: 0.0121 54/126 [===========>..................] - ETA: 0s - loss: 2.8894e-04 - mean_absolute_error: 0.0121 80/126 [==================>...........] - ETA: 0s - loss: 2.6532e-04 - mean_absolute_error: 0.0117106/126 [========================>.....] - ETA: 0s - loss: 2.6403e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6385e-04 - mean_absolute_error: 0.0118 - val_loss: 1.6873e-04 - val_mean_absolute_error: 0.0106
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 2.7559e-04 - mean_absolute_error: 0.0117 28/126 [=====>........................] - ETA: 0s - loss: 2.6696e-04 - mean_absolute_error: 0.0114 55/126 [============>.................] - ETA: 0s - loss: 3.0331e-04 - mean_absolute_error: 0.0122 80/126 [==================>...........] - ETA: 0s - loss: 2.8122e-04 - mean_absolute_error: 0.0120107/126 [========================>.....] - ETA: 0s - loss: 2.9148e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.8018e-04 - mean_absolute_error: 0.0121 - val_loss: 1.2359e-04 - val_mean_absolute_error: 0.0087
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 3.4601e-04 - mean_absolute_error: 0.0128 28/126 [=====>........................] - ETA: 0s - loss: 2.9413e-04 - mean_absolute_error: 0.0126 55/126 [============>.................] - ETA: 0s - loss: 2.8356e-04 - mean_absolute_error: 0.0125 82/126 [==================>...........] - ETA: 0s - loss: 2.6957e-04 - mean_absolute_error: 0.0121108/126 [========================>.....] - ETA: 0s - loss: 2.6302e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6167e-04 - mean_absolute_error: 0.0117 - val_loss: 1.4405e-04 - val_mean_absolute_error: 0.0094
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 2.3991e-04 - mean_absolute_error: 0.0109 27/126 [=====>........................] - ETA: 0s - loss: 2.3004e-04 - mean_absolute_error: 0.0114 53/126 [===========>..................] - ETA: 0s - loss: 2.7219e-04 - mean_absolute_error: 0.0124 79/126 [=================>............] - ETA: 0s - loss: 2.8825e-04 - mean_absolute_error: 0.0125104/126 [=======================>......] - ETA: 0s - loss: 2.9034e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 2ms/step - loss: 2.8294e-04 - mean_absolute_error: 0.0124 - val_loss: 1.5166e-04 - val_mean_absolute_error: 0.0100
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 2.2595e-04 - mean_absolute_error: 0.0101 27/126 [=====>........................] - ETA: 0s - loss: 2.1671e-04 - mean_absolute_error: 0.0107 55/126 [============>.................] - ETA: 0s - loss: 2.6735e-04 - mean_absolute_error: 0.0118 81/126 [==================>...........] - ETA: 0s - loss: 2.5596e-04 - mean_absolute_error: 0.0114107/126 [========================>.....] - ETA: 0s - loss: 2.6713e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6580e-04 - mean_absolute_error: 0.0117 - val_loss: 2.2433e-04 - val_mean_absolute_error: 0.0121
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 1.9780e-04 - mean_absolute_error: 0.0113 27/126 [=====>........................] - ETA: 0s - loss: 2.7057e-04 - mean_absolute_error: 0.0121 53/126 [===========>..................] - ETA: 0s - loss: 2.5533e-04 - mean_absolute_error: 0.0116 78/126 [=================>............] - ETA: 0s - loss: 2.6541e-04 - mean_absolute_error: 0.0117104/126 [=======================>......] - ETA: 0s - loss: 2.5856e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.7535e-04 - mean_absolute_error: 0.0121 - val_loss: 5.3019e-04 - val_mean_absolute_error: 0.0205
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 8.8580e-04 - mean_absolute_error: 0.0222 27/126 [=====>........................] - ETA: 0s - loss: 3.2131e-04 - mean_absolute_error: 0.0139 54/126 [===========>..................] - ETA: 0s - loss: 2.8662e-04 - mean_absolute_error: 0.0127 78/126 [=================>............] - ETA: 0s - loss: 2.7646e-04 - mean_absolute_error: 0.0124104/126 [=======================>......] - ETA: 0s - loss: 2.7608e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.8031e-04 - mean_absolute_error: 0.0122 - val_loss: 4.0287e-04 - val_mean_absolute_error: 0.0173
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 3.8955e-04 - mean_absolute_error: 0.0161 27/126 [=====>........................] - ETA: 0s - loss: 3.3193e-04 - mean_absolute_error: 0.0129 52/126 [===========>..................] - ETA: 0s - loss: 3.1387e-04 - mean_absolute_error: 0.0129 78/126 [=================>............] - ETA: 0s - loss: 3.0694e-04 - mean_absolute_error: 0.0128105/126 [========================>.....] - ETA: 0s - loss: 2.9547e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 2ms/step - loss: 2.8684e-04 - mean_absolute_error: 0.0124 - val_loss: 1.2282e-04 - val_mean_absolute_error: 0.0087
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 2.6694e-04 - mean_absolute_error: 0.0121 27/126 [=====>........................] - ETA: 0s - loss: 2.6192e-04 - mean_absolute_error: 0.0116 53/126 [===========>..................] - ETA: 0s - loss: 2.7204e-04 - mean_absolute_error: 0.0120 79/126 [=================>............] - ETA: 0s - loss: 3.1496e-04 - mean_absolute_error: 0.0131105/126 [========================>.....] - ETA: 0s - loss: 3.1250e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 3.0441e-04 - mean_absolute_error: 0.0129 - val_loss: 1.2702e-04 - val_mean_absolute_error: 0.0090
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 2.2845e-04 - mean_absolute_error: 0.0119 27/126 [=====>........................] - ETA: 0s - loss: 3.1838e-04 - mean_absolute_error: 0.0130 54/126 [===========>..................] - ETA: 0s - loss: 2.8925e-04 - mean_absolute_error: 0.0123 81/126 [==================>...........] - ETA: 0s - loss: 2.6793e-04 - mean_absolute_error: 0.0118107/126 [========================>.....] - ETA: 0s - loss: 2.6407e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6028e-04 - mean_absolute_error: 0.0117 - val_loss: 1.2035e-04 - val_mean_absolute_error: 0.0086
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 2.4846e-04 - mean_absolute_error: 0.0110 26/126 [=====>........................] - ETA: 0s - loss: 2.8175e-04 - mean_absolute_error: 0.0116 52/126 [===========>..................] - ETA: 0s - loss: 2.8326e-04 - mean_absolute_error: 0.0120 78/126 [=================>............] - ETA: 0s - loss: 2.8896e-04 - mean_absolute_error: 0.0123104/126 [=======================>......] - ETA: 0s - loss: 2.8521e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 3.1578e-04 - mean_absolute_error: 0.0131 - val_loss: 3.0284e-04 - val_mean_absolute_error: 0.0145
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 7.7120e-04 - mean_absolute_error: 0.0240 28/126 [=====>........................] - ETA: 0s - loss: 2.5753e-04 - mean_absolute_error: 0.0117 54/126 [===========>..................] - ETA: 0s - loss: 2.6603e-04 - mean_absolute_error: 0.0118 80/126 [==================>...........] - ETA: 0s - loss: 2.6904e-04 - mean_absolute_error: 0.0116106/126 [========================>.....] - ETA: 0s - loss: 2.7037e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6859e-04 - mean_absolute_error: 0.0119 - val_loss: 1.9111e-04 - val_mean_absolute_error: 0.0114
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 3.0006e-04 - mean_absolute_error: 0.0132 28/126 [=====>........................] - ETA: 0s - loss: 2.6036e-04 - mean_absolute_error: 0.0120 54/126 [===========>..................] - ETA: 0s - loss: 2.6440e-04 - mean_absolute_error: 0.0122 81/126 [==================>...........] - ETA: 0s - loss: 2.6138e-04 - mean_absolute_error: 0.0120105/126 [========================>.....] - ETA: 0s - loss: 2.7404e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.5962e-04 - mean_absolute_error: 0.0118 - val_loss: 1.4309e-04 - val_mean_absolute_error: 0.0097
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 4.0890e-04 - mean_absolute_error: 0.0154 27/126 [=====>........................] - ETA: 0s - loss: 2.8990e-04 - mean_absolute_error: 0.0118 54/126 [===========>..................] - ETA: 0s - loss: 2.8805e-04 - mean_absolute_error: 0.0119 80/126 [==================>...........] - ETA: 0s - loss: 2.6614e-04 - mean_absolute_error: 0.0117105/126 [========================>.....] - ETA: 0s - loss: 2.4571e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.5519e-04 - mean_absolute_error: 0.0114 - val_loss: 1.5773e-04 - val_mean_absolute_error: 0.0098
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 1.3825e-04 - mean_absolute_error: 0.0095 27/126 [=====>........................] - ETA: 0s - loss: 2.6845e-04 - mean_absolute_error: 0.0120 52/126 [===========>..................] - ETA: 0s - loss: 2.8930e-04 - mean_absolute_error: 0.0120 76/126 [=================>............] - ETA: 0s - loss: 2.9965e-04 - mean_absolute_error: 0.0125102/126 [=======================>......] - ETA: 0s - loss: 2.8374e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.7800e-04 - mean_absolute_error: 0.0122 - val_loss: 1.3850e-04 - val_mean_absolute_error: 0.0095
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 1.7199e-04 - mean_absolute_error: 0.0111 27/126 [=====>........................] - ETA: 0s - loss: 2.3082e-04 - mean_absolute_error: 0.0112 52/126 [===========>..................] - ETA: 0s - loss: 2.6874e-04 - mean_absolute_error: 0.0118 78/126 [=================>............] - ETA: 0s - loss: 2.8020e-04 - mean_absolute_error: 0.0121104/126 [=======================>......] - ETA: 0s - loss: 2.7799e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7262e-04 - mean_absolute_error: 0.0120 - val_loss: 1.3147e-04 - val_mean_absolute_error: 0.0089
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 9.2754e-05 - mean_absolute_error: 0.0082 27/126 [=====>........................] - ETA: 0s - loss: 2.7868e-04 - mean_absolute_error: 0.0123 53/126 [===========>..................] - ETA: 0s - loss: 3.0815e-04 - mean_absolute_error: 0.0128 80/126 [==================>...........] - ETA: 0s - loss: 2.8126e-04 - mean_absolute_error: 0.0123106/126 [========================>.....] - ETA: 0s - loss: 2.6569e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7552e-04 - mean_absolute_error: 0.0122 - val_loss: 2.9119e-04 - val_mean_absolute_error: 0.0142
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 3.4356e-04 - mean_absolute_error: 0.0138 25/126 [====>.........................] - ETA: 0s - loss: 2.8013e-04 - mean_absolute_error: 0.0126 51/126 [===========>..................] - ETA: 0s - loss: 2.6195e-04 - mean_absolute_error: 0.0118 78/126 [=================>............] - ETA: 0s - loss: 3.1341e-04 - mean_absolute_error: 0.0129104/126 [=======================>......] - ETA: 0s - loss: 3.1509e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 3.2344e-04 - mean_absolute_error: 0.0133 - val_loss: 3.4362e-04 - val_mean_absolute_error: 0.0158
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 3.6727e-04 - mean_absolute_error: 0.0150 26/126 [=====>........................] - ETA: 0s - loss: 3.4555e-04 - mean_absolute_error: 0.0140 52/126 [===========>..................] - ETA: 0s - loss: 3.1595e-04 - mean_absolute_error: 0.0131 77/126 [=================>............] - ETA: 0s - loss: 2.9715e-04 - mean_absolute_error: 0.0126102/126 [=======================>......] - ETA: 0s - loss: 2.8621e-04 - mean_absolute_error: 0.0124126/126 [==============================] - 0s 2ms/step - loss: 2.7942e-04 - mean_absolute_error: 0.0123 - val_loss: 1.8198e-04 - val_mean_absolute_error: 0.0107
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 4.1857e-04 - mean_absolute_error: 0.0167 27/126 [=====>........................] - ETA: 0s - loss: 2.7285e-04 - mean_absolute_error: 0.0122 53/126 [===========>..................] - ETA: 0s - loss: 2.4843e-04 - mean_absolute_error: 0.0116 79/126 [=================>............] - ETA: 0s - loss: 2.5139e-04 - mean_absolute_error: 0.0115105/126 [========================>.....] - ETA: 0s - loss: 2.4395e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.4628e-04 - mean_absolute_error: 0.0113 - val_loss: 1.2055e-04 - val_mean_absolute_error: 0.0085
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 1.0402e-04 - mean_absolute_error: 0.0082 27/126 [=====>........................] - ETA: 0s - loss: 2.5319e-04 - mean_absolute_error: 0.0114 53/126 [===========>..................] - ETA: 0s - loss: 2.5691e-04 - mean_absolute_error: 0.0114 78/126 [=================>............] - ETA: 0s - loss: 2.7410e-04 - mean_absolute_error: 0.0119104/126 [=======================>......] - ETA: 0s - loss: 2.6273e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5837e-04 - mean_absolute_error: 0.0116 - val_loss: 2.2313e-04 - val_mean_absolute_error: 0.0121
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 4.6478e-04 - mean_absolute_error: 0.0154 27/126 [=====>........................] - ETA: 0s - loss: 2.1747e-04 - mean_absolute_error: 0.0109 53/126 [===========>..................] - ETA: 0s - loss: 2.3325e-04 - mean_absolute_error: 0.0113 79/126 [=================>............] - ETA: 0s - loss: 2.6007e-04 - mean_absolute_error: 0.0117104/126 [=======================>......] - ETA: 0s - loss: 2.6010e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6324e-04 - mean_absolute_error: 0.0118 - val_loss: 2.9269e-04 - val_mean_absolute_error: 0.0143
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 6.4620e-04 - mean_absolute_error: 0.0207 27/126 [=====>........................] - ETA: 0s - loss: 2.3540e-04 - mean_absolute_error: 0.0115 53/126 [===========>..................] - ETA: 0s - loss: 2.5651e-04 - mean_absolute_error: 0.0118 79/126 [=================>............] - ETA: 0s - loss: 3.0280e-04 - mean_absolute_error: 0.0127104/126 [=======================>......] - ETA: 0s - loss: 3.0680e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 2.9820e-04 - mean_absolute_error: 0.0128 - val_loss: 1.3650e-04 - val_mean_absolute_error: 0.0094
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 9.6701e-05 - mean_absolute_error: 0.0080 28/126 [=====>........................] - ETA: 0s - loss: 3.0767e-04 - mean_absolute_error: 0.0130 53/126 [===========>..................] - ETA: 0s - loss: 3.0372e-04 - mean_absolute_error: 0.0130 79/126 [=================>............] - ETA: 0s - loss: 2.9181e-04 - mean_absolute_error: 0.0125104/126 [=======================>......] - ETA: 0s - loss: 2.7757e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.7438e-04 - mean_absolute_error: 0.0121 - val_loss: 1.4915e-04 - val_mean_absolute_error: 0.0095
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 2.1463e-04 - mean_absolute_error: 0.0109 26/126 [=====>........................] - ETA: 0s - loss: 3.0122e-04 - mean_absolute_error: 0.0122 52/126 [===========>..................] - ETA: 0s - loss: 2.6630e-04 - mean_absolute_error: 0.0116 78/126 [=================>............] - ETA: 0s - loss: 2.6777e-04 - mean_absolute_error: 0.0118104/126 [=======================>......] - ETA: 0s - loss: 2.5892e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5471e-04 - mean_absolute_error: 0.0116 - val_loss: 1.4057e-04 - val_mean_absolute_error: 0.0093
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 1.6019e-04 - mean_absolute_error: 0.0096 27/126 [=====>........................] - ETA: 0s - loss: 2.8823e-04 - mean_absolute_error: 0.0129 53/126 [===========>..................] - ETA: 0s - loss: 2.5285e-04 - mean_absolute_error: 0.0119 78/126 [=================>............] - ETA: 0s - loss: 2.5349e-04 - mean_absolute_error: 0.0117104/126 [=======================>......] - ETA: 0s - loss: 2.6362e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.8324e-04 - mean_absolute_error: 0.0123 - val_loss: 1.2517e-04 - val_mean_absolute_error: 0.0089
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 2.6785e-04 - mean_absolute_error: 0.0109 27/126 [=====>........................] - ETA: 0s - loss: 2.3551e-04 - mean_absolute_error: 0.0109 53/126 [===========>..................] - ETA: 0s - loss: 2.3045e-04 - mean_absolute_error: 0.0110 80/126 [==================>...........] - ETA: 0s - loss: 2.4267e-04 - mean_absolute_error: 0.0111106/126 [========================>.....] - ETA: 0s - loss: 2.4390e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3611e-04 - mean_absolute_error: 0.0110 - val_loss: 1.1267e-04 - val_mean_absolute_error: 0.0083
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 2.4020e-04 - mean_absolute_error: 0.0110 28/126 [=====>........................] - ETA: 0s - loss: 2.5375e-04 - mean_absolute_error: 0.0115 54/126 [===========>..................] - ETA: 0s - loss: 2.7058e-04 - mean_absolute_error: 0.0121 79/126 [=================>............] - ETA: 0s - loss: 2.5589e-04 - mean_absolute_error: 0.0118104/126 [=======================>......] - ETA: 0s - loss: 2.5815e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6819e-04 - mean_absolute_error: 0.0120 - val_loss: 3.6609e-04 - val_mean_absolute_error: 0.0165
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 4.3240e-04 - mean_absolute_error: 0.0170 24/126 [====>.........................] - ETA: 0s - loss: 3.6104e-04 - mean_absolute_error: 0.0146 47/126 [==========>...................] - ETA: 0s - loss: 3.3410e-04 - mean_absolute_error: 0.0137 69/126 [===============>..............] - ETA: 0s - loss: 2.9566e-04 - mean_absolute_error: 0.0127 91/126 [====================>.........] - ETA: 0s - loss: 2.8013e-04 - mean_absolute_error: 0.0123112/126 [=========================>....] - ETA: 0s - loss: 2.8350e-04 - mean_absolute_error: 0.0124126/126 [==============================] - 0s 3ms/step - loss: 2.8256e-04 - mean_absolute_error: 0.0125 - val_loss: 2.6630e-04 - val_mean_absolute_error: 0.0136
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 2.6535e-04 - mean_absolute_error: 0.0137 23/126 [====>.........................] - ETA: 0s - loss: 2.6964e-04 - mean_absolute_error: 0.0121 44/126 [=========>....................] - ETA: 0s - loss: 2.6575e-04 - mean_absolute_error: 0.0118 66/126 [==============>...............] - ETA: 0s - loss: 2.5476e-04 - mean_absolute_error: 0.0116 89/126 [====================>.........] - ETA: 0s - loss: 2.4915e-04 - mean_absolute_error: 0.0115111/126 [=========================>....] - ETA: 0s - loss: 2.5308e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 3ms/step - loss: 2.6517e-04 - mean_absolute_error: 0.0118 - val_loss: 1.2107e-04 - val_mean_absolute_error: 0.0085
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 2.2608e-04 - mean_absolute_error: 0.0106 27/126 [=====>........................] - ETA: 0s - loss: 2.7495e-04 - mean_absolute_error: 0.0123 53/126 [===========>..................] - ETA: 0s - loss: 3.0902e-04 - mean_absolute_error: 0.0128 78/126 [=================>............] - ETA: 0s - loss: 3.0649e-04 - mean_absolute_error: 0.0129103/126 [=======================>......] - ETA: 0s - loss: 2.8718e-04 - mean_absolute_error: 0.0124126/126 [==============================] - 0s 2ms/step - loss: 2.9209e-04 - mean_absolute_error: 0.0126 - val_loss: 1.1125e-04 - val_mean_absolute_error: 0.0082
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 1.8033e-04 - mean_absolute_error: 0.0106 28/126 [=====>........................] - ETA: 0s - loss: 2.5662e-04 - mean_absolute_error: 0.0117 55/126 [============>.................] - ETA: 0s - loss: 2.4544e-04 - mean_absolute_error: 0.0112 82/126 [==================>...........] - ETA: 0s - loss: 2.4537e-04 - mean_absolute_error: 0.0112108/126 [========================>.....] - ETA: 0s - loss: 2.3624e-04 - mean_absolute_error: 0.0110126/126 [==============================] - 0s 2ms/step - loss: 2.3044e-04 - mean_absolute_error: 0.0109 - val_loss: 1.1289e-04 - val_mean_absolute_error: 0.0084
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 1.5231e-04 - mean_absolute_error: 0.0100 27/126 [=====>........................] - ETA: 0s - loss: 2.8756e-04 - mean_absolute_error: 0.0119 51/126 [===========>..................] - ETA: 0s - loss: 3.1130e-04 - mean_absolute_error: 0.0130 76/126 [=================>............] - ETA: 0s - loss: 2.8411e-04 - mean_absolute_error: 0.0124102/126 [=======================>......] - ETA: 0s - loss: 2.8316e-04 - mean_absolute_error: 0.0125126/126 [==============================] - 0s 2ms/step - loss: 2.8345e-04 - mean_absolute_error: 0.0124 - val_loss: 1.5749e-04 - val_mean_absolute_error: 0.0102
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 2.5633e-04 - mean_absolute_error: 0.0124 27/126 [=====>........................] - ETA: 0s - loss: 2.3153e-04 - mean_absolute_error: 0.0113 54/126 [===========>..................] - ETA: 0s - loss: 3.2949e-04 - mean_absolute_error: 0.0134 81/126 [==================>...........] - ETA: 0s - loss: 3.0884e-04 - mean_absolute_error: 0.0128108/126 [========================>.....] - ETA: 0s - loss: 2.7392e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.6567e-04 - mean_absolute_error: 0.0119 - val_loss: 1.0987e-04 - val_mean_absolute_error: 0.0082
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 1.6215e-04 - mean_absolute_error: 0.0090 27/126 [=====>........................] - ETA: 0s - loss: 2.8033e-04 - mean_absolute_error: 0.0120 53/126 [===========>..................] - ETA: 0s - loss: 2.4495e-04 - mean_absolute_error: 0.0114 79/126 [=================>............] - ETA: 0s - loss: 2.3559e-04 - mean_absolute_error: 0.0112105/126 [========================>.....] - ETA: 0s - loss: 2.4517e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.4732e-04 - mean_absolute_error: 0.0114 - val_loss: 1.1069e-04 - val_mean_absolute_error: 0.0082
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 1.0240e-04 - mean_absolute_error: 0.0079 27/126 [=====>........................] - ETA: 0s - loss: 3.8497e-04 - mean_absolute_error: 0.0140 52/126 [===========>..................] - ETA: 0s - loss: 4.0954e-04 - mean_absolute_error: 0.0149 78/126 [=================>............] - ETA: 0s - loss: 3.8259e-04 - mean_absolute_error: 0.0146104/126 [=======================>......] - ETA: 0s - loss: 3.3464e-04 - mean_absolute_error: 0.0135126/126 [==============================] - 0s 2ms/step - loss: 3.1775e-04 - mean_absolute_error: 0.0131 - val_loss: 1.1352e-04 - val_mean_absolute_error: 0.0083
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 2.2910e-04 - mean_absolute_error: 0.0106 27/126 [=====>........................] - ETA: 0s - loss: 2.9299e-04 - mean_absolute_error: 0.0117 52/126 [===========>..................] - ETA: 0s - loss: 2.8003e-04 - mean_absolute_error: 0.0119 77/126 [=================>............] - ETA: 0s - loss: 3.1912e-04 - mean_absolute_error: 0.0132102/126 [=======================>......] - ETA: 0s - loss: 2.9808e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 2ms/step - loss: 2.8023e-04 - mean_absolute_error: 0.0122 - val_loss: 1.2968e-04 - val_mean_absolute_error: 0.0089
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 1.3480e-04 - mean_absolute_error: 0.0089 27/126 [=====>........................] - ETA: 0s - loss: 2.2448e-04 - mean_absolute_error: 0.0105 53/126 [===========>..................] - ETA: 0s - loss: 2.3872e-04 - mean_absolute_error: 0.0109 77/126 [=================>............] - ETA: 0s - loss: 2.3752e-04 - mean_absolute_error: 0.0110103/126 [=======================>......] - ETA: 0s - loss: 2.4391e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.3981e-04 - mean_absolute_error: 0.0112 - val_loss: 1.1145e-04 - val_mean_absolute_error: 0.0082
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 2.5188e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.9762e-04 - mean_absolute_error: 0.0100 54/126 [===========>..................] - ETA: 0s - loss: 2.0322e-04 - mean_absolute_error: 0.0103 80/126 [==================>...........] - ETA: 0s - loss: 2.1992e-04 - mean_absolute_error: 0.0107107/126 [========================>.....] - ETA: 0s - loss: 2.3110e-04 - mean_absolute_error: 0.0110126/126 [==============================] - 0s 2ms/step - loss: 2.2967e-04 - mean_absolute_error: 0.0109 - val_loss: 3.4841e-04 - val_mean_absolute_error: 0.0163
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 3.6506e-04 - mean_absolute_error: 0.0168 27/126 [=====>........................] - ETA: 0s - loss: 2.3000e-04 - mean_absolute_error: 0.0113 51/126 [===========>..................] - ETA: 0s - loss: 2.7057e-04 - mean_absolute_error: 0.0125 77/126 [=================>............] - ETA: 0s - loss: 2.7908e-04 - mean_absolute_error: 0.0127103/126 [=======================>......] - ETA: 0s - loss: 2.7565e-04 - mean_absolute_error: 0.0125126/126 [==============================] - 0s 2ms/step - loss: 2.8699e-04 - mean_absolute_error: 0.0127 - val_loss: 1.0922e-04 - val_mean_absolute_error: 0.0083
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 1.9317e-04 - mean_absolute_error: 0.0110 27/126 [=====>........................] - ETA: 0s - loss: 2.6387e-04 - mean_absolute_error: 0.0118 54/126 [===========>..................] - ETA: 0s - loss: 2.7307e-04 - mean_absolute_error: 0.0120 80/126 [==================>...........] - ETA: 0s - loss: 2.5404e-04 - mean_absolute_error: 0.0116106/126 [========================>.....] - ETA: 0s - loss: 2.5563e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5081e-04 - mean_absolute_error: 0.0116 - val_loss: 1.1329e-04 - val_mean_absolute_error: 0.0085
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 1.7409e-04 - mean_absolute_error: 0.0111 26/126 [=====>........................] - ETA: 0s - loss: 2.1537e-04 - mean_absolute_error: 0.0106 52/126 [===========>..................] - ETA: 0s - loss: 2.1288e-04 - mean_absolute_error: 0.0104 78/126 [=================>............] - ETA: 0s - loss: 2.3393e-04 - mean_absolute_error: 0.0111103/126 [=======================>......] - ETA: 0s - loss: 2.3009e-04 - mean_absolute_error: 0.0109126/126 [==============================] - 0s 2ms/step - loss: 2.2873e-04 - mean_absolute_error: 0.0109 - val_loss: 1.3892e-04 - val_mean_absolute_error: 0.0092
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 1.9340e-04 - mean_absolute_error: 0.0108 26/126 [=====>........................] - ETA: 0s - loss: 2.3222e-04 - mean_absolute_error: 0.0116 52/126 [===========>..................] - ETA: 0s - loss: 2.4353e-04 - mean_absolute_error: 0.0116 76/126 [=================>............] - ETA: 0s - loss: 2.5894e-04 - mean_absolute_error: 0.0116101/126 [=======================>......] - ETA: 0s - loss: 2.6079e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.5501e-04 - mean_absolute_error: 0.0117 - val_loss: 1.9710e-04 - val_mean_absolute_error: 0.0113
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 7.3404e-04 - mean_absolute_error: 0.0161 26/126 [=====>........................] - ETA: 0s - loss: 2.9105e-04 - mean_absolute_error: 0.0126 51/126 [===========>..................] - ETA: 0s - loss: 2.4955e-04 - mean_absolute_error: 0.0115 77/126 [=================>............] - ETA: 0s - loss: 2.4249e-04 - mean_absolute_error: 0.0113102/126 [=======================>......] - ETA: 0s - loss: 2.3566e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 2ms/step - loss: 2.3669e-04 - mean_absolute_error: 0.0112 - val_loss: 1.3358e-04 - val_mean_absolute_error: 0.0090
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 3.5746e-04 - mean_absolute_error: 0.0135 27/126 [=====>........................] - ETA: 0s - loss: 2.2060e-04 - mean_absolute_error: 0.0106 51/126 [===========>..................] - ETA: 0s - loss: 2.1002e-04 - mean_absolute_error: 0.0106 77/126 [=================>............] - ETA: 0s - loss: 2.2065e-04 - mean_absolute_error: 0.0107102/126 [=======================>......] - ETA: 0s - loss: 2.1553e-04 - mean_absolute_error: 0.0107125/126 [============================>.] - ETA: 0s - loss: 2.3716e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 2ms/step - loss: 2.3721e-04 - mean_absolute_error: 0.0111 - val_loss: 1.1436e-04 - val_mean_absolute_error: 0.0083
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 2.9857e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 2.6738e-04 - mean_absolute_error: 0.0114 53/126 [===========>..................] - ETA: 0s - loss: 2.4394e-04 - mean_absolute_error: 0.0108 80/126 [==================>...........] - ETA: 0s - loss: 2.4693e-04 - mean_absolute_error: 0.0110107/126 [========================>.....] - ETA: 0s - loss: 2.3212e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 2.3328e-04 - mean_absolute_error: 0.0110 - val_loss: 3.1063e-04 - val_mean_absolute_error: 0.0151
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 6.7456e-04 - mean_absolute_error: 0.0180 27/126 [=====>........................] - ETA: 0s - loss: 3.2188e-04 - mean_absolute_error: 0.0134 53/126 [===========>..................] - ETA: 0s - loss: 2.8479e-04 - mean_absolute_error: 0.0124 79/126 [=================>............] - ETA: 0s - loss: 2.7491e-04 - mean_absolute_error: 0.0120104/126 [=======================>......] - ETA: 0s - loss: 2.5187e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.5443e-04 - mean_absolute_error: 0.0117 - val_loss: 2.8050e-04 - val_mean_absolute_error: 0.0142
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 4.5610e-04 - mean_absolute_error: 0.0153 27/126 [=====>........................] - ETA: 0s - loss: 2.1413e-04 - mean_absolute_error: 0.0109 54/126 [===========>..................] - ETA: 0s - loss: 2.2780e-04 - mean_absolute_error: 0.0110 81/126 [==================>...........] - ETA: 0s - loss: 2.4447e-04 - mean_absolute_error: 0.0113108/126 [========================>.....] - ETA: 0s - loss: 2.4611e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 2.4756e-04 - mean_absolute_error: 0.0115 - val_loss: 1.5479e-04 - val_mean_absolute_error: 0.0102
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 7.6314e-04 - mean_absolute_error: 0.0165 26/126 [=====>........................] - ETA: 0s - loss: 2.8809e-04 - mean_absolute_error: 0.0122 51/126 [===========>..................] - ETA: 0s - loss: 2.7127e-04 - mean_absolute_error: 0.0121 77/126 [=================>............] - ETA: 0s - loss: 2.5707e-04 - mean_absolute_error: 0.0119102/126 [=======================>......] - ETA: 0s - loss: 2.5027e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5400e-04 - mean_absolute_error: 0.0116 - val_loss: 1.0911e-04 - val_mean_absolute_error: 0.0081
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 2.7722e-04 - mean_absolute_error: 0.0118 27/126 [=====>........................] - ETA: 0s - loss: 2.5160e-04 - mean_absolute_error: 0.0113 52/126 [===========>..................] - ETA: 0s - loss: 2.4274e-04 - mean_absolute_error: 0.0113 76/126 [=================>............] - ETA: 0s - loss: 2.4452e-04 - mean_absolute_error: 0.0112101/126 [=======================>......] - ETA: 0s - loss: 2.3547e-04 - mean_absolute_error: 0.0110126/126 [==============================] - ETA: 0s - loss: 2.2725e-04 - mean_absolute_error: 0.0109126/126 [==============================] - 0s 2ms/step - loss: 2.2725e-04 - mean_absolute_error: 0.0109 - val_loss: 2.3686e-04 - val_mean_absolute_error: 0.0130
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 4.1440e-04 - mean_absolute_error: 0.0161 27/126 [=====>........................] - ETA: 0s - loss: 2.4515e-04 - mean_absolute_error: 0.0114 52/126 [===========>..................] - ETA: 0s - loss: 2.3136e-04 - mean_absolute_error: 0.0111 77/126 [=================>............] - ETA: 0s - loss: 2.4649e-04 - mean_absolute_error: 0.0116103/126 [=======================>......] - ETA: 0s - loss: 2.5279e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.4581e-04 - mean_absolute_error: 0.0114 - val_loss: 1.2731e-04 - val_mean_absolute_error: 0.0091
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 2.8698e-04 - mean_absolute_error: 0.0134 27/126 [=====>........................] - ETA: 0s - loss: 2.1060e-04 - mean_absolute_error: 0.0105 52/126 [===========>..................] - ETA: 0s - loss: 2.5140e-04 - mean_absolute_error: 0.0115 78/126 [=================>............] - ETA: 0s - loss: 2.5562e-04 - mean_absolute_error: 0.0116105/126 [========================>.....] - ETA: 0s - loss: 2.4166e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3287e-04 - mean_absolute_error: 0.0111 - val_loss: 1.0323e-04 - val_mean_absolute_error: 0.0079
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 1.8412e-04 - mean_absolute_error: 0.0098 28/126 [=====>........................] - ETA: 0s - loss: 2.4579e-04 - mean_absolute_error: 0.0112 54/126 [===========>..................] - ETA: 0s - loss: 2.2114e-04 - mean_absolute_error: 0.0107 80/126 [==================>...........] - ETA: 0s - loss: 2.1645e-04 - mean_absolute_error: 0.0106107/126 [========================>.....] - ETA: 0s - loss: 2.4168e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.3662e-04 - mean_absolute_error: 0.0112 - val_loss: 1.1796e-04 - val_mean_absolute_error: 0.0084
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 3.6539e-04 - mean_absolute_error: 0.0135 27/126 [=====>........................] - ETA: 0s - loss: 2.4490e-04 - mean_absolute_error: 0.0116 53/126 [===========>..................] - ETA: 0s - loss: 2.2695e-04 - mean_absolute_error: 0.0110 79/126 [=================>............] - ETA: 0s - loss: 2.2943e-04 - mean_absolute_error: 0.0110105/126 [========================>.....] - ETA: 0s - loss: 2.2733e-04 - mean_absolute_error: 0.0110126/126 [==============================] - 0s 2ms/step - loss: 2.2778e-04 - mean_absolute_error: 0.0110 - val_loss: 2.8629e-04 - val_mean_absolute_error: 0.0144
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 2.4602e-04 - mean_absolute_error: 0.0120 27/126 [=====>........................] - ETA: 0s - loss: 1.9845e-04 - mean_absolute_error: 0.0104 53/126 [===========>..................] - ETA: 0s - loss: 2.1300e-04 - mean_absolute_error: 0.0106 80/126 [==================>...........] - ETA: 0s - loss: 2.2501e-04 - mean_absolute_error: 0.0110105/126 [========================>.....] - ETA: 0s - loss: 2.2188e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 2.1933e-04 - mean_absolute_error: 0.0108 - val_loss: 2.2092e-04 - val_mean_absolute_error: 0.0125
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 2.7642e-04 - mean_absolute_error: 0.0136 28/126 [=====>........................] - ETA: 0s - loss: 2.5314e-04 - mean_absolute_error: 0.0113 54/126 [===========>..................] - ETA: 0s - loss: 2.4755e-04 - mean_absolute_error: 0.0117 81/126 [==================>...........] - ETA: 0s - loss: 2.3640e-04 - mean_absolute_error: 0.0112107/126 [========================>.....] - ETA: 0s - loss: 2.3374e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3490e-04 - mean_absolute_error: 0.0112 - val_loss: 1.0057e-04 - val_mean_absolute_error: 0.0078
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 1.5780e-04 - mean_absolute_error: 0.0098 28/126 [=====>........................] - ETA: 0s - loss: 2.4235e-04 - mean_absolute_error: 0.0111 53/126 [===========>..................] - ETA: 0s - loss: 2.1480e-04 - mean_absolute_error: 0.0107 78/126 [=================>............] - ETA: 0s - loss: 2.1445e-04 - mean_absolute_error: 0.0107103/126 [=======================>......] - ETA: 0s - loss: 2.1356e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1453e-04 - mean_absolute_error: 0.0106 - val_loss: 1.0582e-04 - val_mean_absolute_error: 0.0080
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 1.5182e-04 - mean_absolute_error: 0.0100 26/126 [=====>........................] - ETA: 0s - loss: 1.9683e-04 - mean_absolute_error: 0.0102 52/126 [===========>..................] - ETA: 0s - loss: 2.2566e-04 - mean_absolute_error: 0.0111 78/126 [=================>............] - ETA: 0s - loss: 2.4201e-04 - mean_absolute_error: 0.0115103/126 [=======================>......] - ETA: 0s - loss: 2.6272e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.4960e-04 - mean_absolute_error: 0.0116 - val_loss: 1.0608e-04 - val_mean_absolute_error: 0.0082
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 2.7944e-04 - mean_absolute_error: 0.0117 26/126 [=====>........................] - ETA: 0s - loss: 2.8401e-04 - mean_absolute_error: 0.0125 52/126 [===========>..................] - ETA: 0s - loss: 2.7494e-04 - mean_absolute_error: 0.0121 78/126 [=================>............] - ETA: 0s - loss: 2.5643e-04 - mean_absolute_error: 0.0118104/126 [=======================>......] - ETA: 0s - loss: 2.3960e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.2919e-04 - mean_absolute_error: 0.0111 - val_loss: 9.8338e-05 - val_mean_absolute_error: 0.0077
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 2.8789e-04 - mean_absolute_error: 0.0110 28/126 [=====>........................] - ETA: 0s - loss: 2.0269e-04 - mean_absolute_error: 0.0105 54/126 [===========>..................] - ETA: 0s - loss: 2.1123e-04 - mean_absolute_error: 0.0104 80/126 [==================>...........] - ETA: 0s - loss: 2.0217e-04 - mean_absolute_error: 0.0103106/126 [========================>.....] - ETA: 0s - loss: 1.9746e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 2ms/step - loss: 2.0722e-04 - mean_absolute_error: 0.0104 - val_loss: 1.1068e-04 - val_mean_absolute_error: 0.0082
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 2.2459e-04 - mean_absolute_error: 0.0109 28/126 [=====>........................] - ETA: 0s - loss: 2.0814e-04 - mean_absolute_error: 0.0102 53/126 [===========>..................] - ETA: 0s - loss: 2.1036e-04 - mean_absolute_error: 0.0104 79/126 [=================>............] - ETA: 0s - loss: 2.2232e-04 - mean_absolute_error: 0.0107105/126 [========================>.....] - ETA: 0s - loss: 2.1492e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1008e-04 - mean_absolute_error: 0.0105 - val_loss: 1.0953e-04 - val_mean_absolute_error: 0.0081
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 8.3455e-05 - mean_absolute_error: 0.0070 27/126 [=====>........................] - ETA: 0s - loss: 2.0534e-04 - mean_absolute_error: 0.0109 53/126 [===========>..................] - ETA: 0s - loss: 2.2150e-04 - mean_absolute_error: 0.0112 78/126 [=================>............] - ETA: 0s - loss: 2.2720e-04 - mean_absolute_error: 0.0114103/126 [=======================>......] - ETA: 0s - loss: 2.2935e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.2606e-04 - mean_absolute_error: 0.0110 - val_loss: 2.0769e-04 - val_mean_absolute_error: 0.0119
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 2.5967e-04 - mean_absolute_error: 0.0125 27/126 [=====>........................] - ETA: 0s - loss: 1.7774e-04 - mean_absolute_error: 0.0099 53/126 [===========>..................] - ETA: 0s - loss: 1.7335e-04 - mean_absolute_error: 0.0096 79/126 [=================>............] - ETA: 0s - loss: 1.8442e-04 - mean_absolute_error: 0.0099105/126 [========================>.....] - ETA: 0s - loss: 1.9572e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 2.0980e-04 - mean_absolute_error: 0.0104 - val_loss: 1.6354e-04 - val_mean_absolute_error: 0.0103
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 2.4171e-04 - mean_absolute_error: 0.0129 28/126 [=====>........................] - ETA: 0s - loss: 2.0517e-04 - mean_absolute_error: 0.0104 54/126 [===========>..................] - ETA: 0s - loss: 2.1465e-04 - mean_absolute_error: 0.0106 80/126 [==================>...........] - ETA: 0s - loss: 2.1793e-04 - mean_absolute_error: 0.0106106/126 [========================>.....] - ETA: 0s - loss: 2.1566e-04 - mean_absolute_error: 0.0107126/126 [==============================] - 0s 2ms/step - loss: 2.1514e-04 - mean_absolute_error: 0.0106 - val_loss: 1.1147e-04 - val_mean_absolute_error: 0.0082
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 1.8449e-04 - mean_absolute_error: 0.0100 26/126 [=====>........................] - ETA: 0s - loss: 2.3217e-04 - mean_absolute_error: 0.0114 52/126 [===========>..................] - ETA: 0s - loss: 2.2770e-04 - mean_absolute_error: 0.0113 77/126 [=================>............] - ETA: 0s - loss: 2.2999e-04 - mean_absolute_error: 0.0113103/126 [=======================>......] - ETA: 0s - loss: 2.3274e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 2ms/step - loss: 2.2306e-04 - mean_absolute_error: 0.0109 - val_loss: 2.4324e-04 - val_mean_absolute_error: 0.0131
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 2.6031e-04 - mean_absolute_error: 0.0144 25/126 [====>.........................] - ETA: 0s - loss: 2.7761e-04 - mean_absolute_error: 0.0130 51/126 [===========>..................] - ETA: 0s - loss: 2.8497e-04 - mean_absolute_error: 0.0131 76/126 [=================>............] - ETA: 0s - loss: 2.9820e-04 - mean_absolute_error: 0.0133102/126 [=======================>......] - ETA: 0s - loss: 2.7304e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 2ms/step - loss: 2.5881e-04 - mean_absolute_error: 0.0121 - val_loss: 1.0770e-04 - val_mean_absolute_error: 0.0081
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 9.4635e-05 - mean_absolute_error: 0.0078 27/126 [=====>........................] - ETA: 0s - loss: 1.9631e-04 - mean_absolute_error: 0.0105 54/126 [===========>..................] - ETA: 0s - loss: 1.8419e-04 - mean_absolute_error: 0.0100 80/126 [==================>...........] - ETA: 0s - loss: 2.0238e-04 - mean_absolute_error: 0.0102106/126 [========================>.....] - ETA: 0s - loss: 2.1433e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1773e-04 - mean_absolute_error: 0.0107 - val_loss: 2.6424e-04 - val_mean_absolute_error: 0.0138
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 3.8021e-04 - mean_absolute_error: 0.0159 27/126 [=====>........................] - ETA: 0s - loss: 2.5265e-04 - mean_absolute_error: 0.0120 54/126 [===========>..................] - ETA: 0s - loss: 2.5166e-04 - mean_absolute_error: 0.0119 79/126 [=================>............] - ETA: 0s - loss: 2.4056e-04 - mean_absolute_error: 0.0115105/126 [========================>.....] - ETA: 0s - loss: 2.3894e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 2.4016e-04 - mean_absolute_error: 0.0115 - val_loss: 2.0638e-04 - val_mean_absolute_error: 0.0119
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 3.9002e-04 - mean_absolute_error: 0.0151 27/126 [=====>........................] - ETA: 0s - loss: 2.0708e-04 - mean_absolute_error: 0.0105 54/126 [===========>..................] - ETA: 0s - loss: 1.8922e-04 - mean_absolute_error: 0.0099 79/126 [=================>............] - ETA: 0s - loss: 2.2363e-04 - mean_absolute_error: 0.0108105/126 [========================>.....] - ETA: 0s - loss: 2.2501e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 2.2412e-04 - mean_absolute_error: 0.0109 - val_loss: 1.7910e-04 - val_mean_absolute_error: 0.0111
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 1.7123e-04 - mean_absolute_error: 0.0112 27/126 [=====>........................] - ETA: 0s - loss: 2.2542e-04 - mean_absolute_error: 0.0108 53/126 [===========>..................] - ETA: 0s - loss: 2.2349e-04 - mean_absolute_error: 0.0107 79/126 [=================>............] - ETA: 0s - loss: 2.2417e-04 - mean_absolute_error: 0.0110103/126 [=======================>......] - ETA: 0s - loss: 2.2271e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 2.1697e-04 - mean_absolute_error: 0.0108 - val_loss: 9.7154e-05 - val_mean_absolute_error: 0.0076
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 1.4258e-04 - mean_absolute_error: 0.0099 27/126 [=====>........................] - ETA: 0s - loss: 1.7757e-04 - mean_absolute_error: 0.0096 53/126 [===========>..................] - ETA: 0s - loss: 1.8490e-04 - mean_absolute_error: 0.0098 79/126 [=================>............] - ETA: 0s - loss: 1.9466e-04 - mean_absolute_error: 0.0101105/126 [========================>.....] - ETA: 0s - loss: 2.0583e-04 - mean_absolute_error: 0.0105126/126 [==============================] - 0s 2ms/step - loss: 2.1619e-04 - mean_absolute_error: 0.0107 - val_loss: 1.4235e-04 - val_mean_absolute_error: 0.0097

<keras.src.callbacks.History at 0x267ce076a10>

After the training and fitting of the Machine Learning model for Austrailia, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Austrailia with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
austrailia_train_pred = austrailia_model.predict(X_austrailia_train).flatten()

plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4)
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Training Set Predictions vs. Observations")
plt.show()

austrailia_val_pred = austrailia_model.predict(X_austrailia_val).flatten()

plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Validation Set Predictions vs. Observations")
plt.show()

austrailia_test_pred = austrailia_model.predict(X_austrailia_test).flatten()

plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 43s 48/126 [==========>...................] - ETA: 0s  95/126 [=====================>........] - ETA: 0s126/126 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Austrailia’s international currency rate with the United States.

```{python}
# Plotting Austrailia's observational (reference) data with the predictions of 
# its Machine Learning Model (as a way to visually inspect the effectiveness of 
# the model) 
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4)
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations")
plt.show()
```

Once Austraila’s data was completely trained and visualized, I worked on Canada’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Canada’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting Canada's data into train, test, and validation sets on 3 mediums: 
# the X-axis, the y-axis, and the indices (represented by dates)
dates_canada_train, X_canada_train, y_canada_train = dates_canada[:percentile_80], X_canada[:percentile_80], y_canada[:percentile_80]
dates_canada_val, X_canada_val, y_canada_val = dates_canada[percentile_80:percentile_90], X_canada[percentile_80:percentile_90], y_canada[percentile_80:percentile_90]
dates_canada_test, X_canada_test, y_canada_test = dates_canada[percentile_90:], X_canada[percentile_90:], y_canada[percentile_90:]

plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Canada's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Canada
canada_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

canada_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

canada_model.fit(X_canada_train, y_canada_train, validation_data=(X_canada_val, y_canada_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:49 - loss: 1.3546 - mean_absolute_error: 1.1487 24/126 [====>.........................] - ETA: 0s - loss: 0.6880 - mean_absolute_error: 0.7796   49/126 [==========>...................] - ETA: 0s - loss: 0.3488 - mean_absolute_error: 0.4483 74/126 [================>.............] - ETA: 0s - loss: 0.2326 - mean_absolute_error: 0.3154 99/126 [======================>.......] - ETA: 0s - loss: 0.1744 - mean_absolute_error: 0.2459124/126 [============================>.] - ETA: 0s - loss: 0.1396 - mean_absolute_error: 0.2038126/126 [==============================] - 2s 5ms/step - loss: 0.1381 - mean_absolute_error: 0.2019 - val_loss: 4.2959e-04 - val_mean_absolute_error: 0.0173
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0014 - mean_absolute_error: 0.0338 28/126 [=====>........................] - ETA: 0s - loss: 0.0015 - mean_absolute_error: 0.0340 54/126 [===========>..................] - ETA: 0s - loss: 0.0014 - mean_absolute_error: 0.0332 80/126 [==================>...........] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0319107/126 [========================>.....] - ETA: 0s - loss: 0.0012 - mean_absolute_error: 0.0305126/126 [==============================] - 0s 2ms/step - loss: 0.0012 - mean_absolute_error: 0.0297 - val_loss: 1.4711e-04 - val_mean_absolute_error: 0.0096
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 6.4373e-04 - mean_absolute_error: 0.0225 28/126 [=====>........................] - ETA: 0s - loss: 6.4664e-04 - mean_absolute_error: 0.0219 54/126 [===========>..................] - ETA: 0s - loss: 6.0963e-04 - mean_absolute_error: 0.0213 80/126 [==================>...........] - ETA: 0s - loss: 5.4550e-04 - mean_absolute_error: 0.0200105/126 [========================>.....] - ETA: 0s - loss: 5.0119e-04 - mean_absolute_error: 0.0190126/126 [==============================] - 0s 2ms/step - loss: 4.6874e-04 - mean_absolute_error: 0.0183 - val_loss: 1.7349e-04 - val_mean_absolute_error: 0.0107
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 3.9479e-04 - mean_absolute_error: 0.0173 28/126 [=====>........................] - ETA: 0s - loss: 2.3435e-04 - mean_absolute_error: 0.0127 54/126 [===========>..................] - ETA: 0s - loss: 2.1777e-04 - mean_absolute_error: 0.0121 80/126 [==================>...........] - ETA: 0s - loss: 1.9770e-04 - mean_absolute_error: 0.0115106/126 [========================>.....] - ETA: 0s - loss: 1.8160e-04 - mean_absolute_error: 0.0110126/126 [==============================] - 0s 2ms/step - loss: 1.7329e-04 - mean_absolute_error: 0.0106 - val_loss: 1.0822e-04 - val_mean_absolute_error: 0.0082
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 1.1589e-04 - mean_absolute_error: 0.0087 28/126 [=====>........................] - ETA: 0s - loss: 1.1467e-04 - mean_absolute_error: 0.0083 54/126 [===========>..................] - ETA: 0s - loss: 1.0420e-04 - mean_absolute_error: 0.0080 79/126 [=================>............] - ETA: 0s - loss: 1.0471e-04 - mean_absolute_error: 0.0079105/126 [========================>.....] - ETA: 0s - loss: 1.0758e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0508e-04 - mean_absolute_error: 0.0079 - val_loss: 1.2361e-04 - val_mean_absolute_error: 0.0089
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 8.9952e-05 - mean_absolute_error: 0.0077 27/126 [=====>........................] - ETA: 0s - loss: 1.0022e-04 - mean_absolute_error: 0.0075 52/126 [===========>..................] - ETA: 0s - loss: 9.7012e-05 - mean_absolute_error: 0.0074 77/126 [=================>............] - ETA: 0s - loss: 9.7637e-05 - mean_absolute_error: 0.0074103/126 [=======================>......] - ETA: 0s - loss: 9.6471e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.5472e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1466e-04 - val_mean_absolute_error: 0.0084
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 5.2517e-05 - mean_absolute_error: 0.0058 27/126 [=====>........................] - ETA: 0s - loss: 9.8134e-05 - mean_absolute_error: 0.0072 53/126 [===========>..................] - ETA: 0s - loss: 9.6780e-05 - mean_absolute_error: 0.0073 79/126 [=================>............] - ETA: 0s - loss: 9.4190e-05 - mean_absolute_error: 0.0073105/126 [========================>.....] - ETA: 0s - loss: 9.5352e-05 - mean_absolute_error: 0.0073126/126 [==============================] - 0s 2ms/step - loss: 9.4516e-05 - mean_absolute_error: 0.0073 - val_loss: 1.1747e-04 - val_mean_absolute_error: 0.0086
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 1.0744e-04 - mean_absolute_error: 0.0086 27/126 [=====>........................] - ETA: 0s - loss: 9.1423e-05 - mean_absolute_error: 0.0074 54/126 [===========>..................] - ETA: 0s - loss: 9.5570e-05 - mean_absolute_error: 0.0074 80/126 [==================>...........] - ETA: 0s - loss: 9.4530e-05 - mean_absolute_error: 0.0074107/126 [========================>.....] - ETA: 0s - loss: 9.6829e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.5350e-05 - mean_absolute_error: 0.0073 - val_loss: 1.1812e-04 - val_mean_absolute_error: 0.0086
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 1.1425e-04 - mean_absolute_error: 0.0082 26/126 [=====>........................] - ETA: 0s - loss: 9.2100e-05 - mean_absolute_error: 0.0074 52/126 [===========>..................] - ETA: 0s - loss: 9.2224e-05 - mean_absolute_error: 0.0073 78/126 [=================>............] - ETA: 0s - loss: 9.0155e-05 - mean_absolute_error: 0.0072104/126 [=======================>......] - ETA: 0s - loss: 9.1788e-05 - mean_absolute_error: 0.0073126/126 [==============================] - 0s 2ms/step - loss: 9.5631e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0942e-04 - val_mean_absolute_error: 0.0081
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 1.0147e-04 - mean_absolute_error: 0.0081 28/126 [=====>........................] - ETA: 0s - loss: 1.0150e-04 - mean_absolute_error: 0.0075 54/126 [===========>..................] - ETA: 0s - loss: 9.7679e-05 - mean_absolute_error: 0.0074 80/126 [==================>...........] - ETA: 0s - loss: 9.8400e-05 - mean_absolute_error: 0.0074105/126 [========================>.....] - ETA: 0s - loss: 9.6562e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.5570e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0747e-04 - val_mean_absolute_error: 0.0081
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 7.1547e-05 - mean_absolute_error: 0.0065 26/126 [=====>........................] - ETA: 0s - loss: 9.5956e-05 - mean_absolute_error: 0.0076 52/126 [===========>..................] - ETA: 0s - loss: 9.7673e-05 - mean_absolute_error: 0.0075 78/126 [=================>............] - ETA: 0s - loss: 9.5784e-05 - mean_absolute_error: 0.0073104/126 [=======================>......] - ETA: 0s - loss: 9.6190e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.5932e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0871e-04 - val_mean_absolute_error: 0.0080
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 7.1176e-05 - mean_absolute_error: 0.0071 28/126 [=====>........................] - ETA: 0s - loss: 9.5641e-05 - mean_absolute_error: 0.0072 53/126 [===========>..................] - ETA: 0s - loss: 9.7071e-05 - mean_absolute_error: 0.0074 80/126 [==================>...........] - ETA: 0s - loss: 9.6816e-05 - mean_absolute_error: 0.0074106/126 [========================>.....] - ETA: 0s - loss: 9.6501e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.6978e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0689e-04 - val_mean_absolute_error: 0.0080
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 5.8118e-05 - mean_absolute_error: 0.0058 28/126 [=====>........................] - ETA: 0s - loss: 9.2891e-05 - mean_absolute_error: 0.0072 55/126 [============>.................] - ETA: 0s - loss: 9.7247e-05 - mean_absolute_error: 0.0074 81/126 [==================>...........] - ETA: 0s - loss: 9.5512e-05 - mean_absolute_error: 0.0074107/126 [========================>.....] - ETA: 0s - loss: 9.6165e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.6977e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0760e-04 - val_mean_absolute_error: 0.0079
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 7.2389e-05 - mean_absolute_error: 0.0066 26/126 [=====>........................] - ETA: 0s - loss: 9.0509e-05 - mean_absolute_error: 0.0072 53/126 [===========>..................] - ETA: 0s - loss: 1.0451e-04 - mean_absolute_error: 0.0077 79/126 [=================>............] - ETA: 0s - loss: 1.0491e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 1.0083e-04 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.8001e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1557e-04 - val_mean_absolute_error: 0.0085
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 8.9756e-05 - mean_absolute_error: 0.0069 28/126 [=====>........................] - ETA: 0s - loss: 1.0320e-04 - mean_absolute_error: 0.0075 54/126 [===========>..................] - ETA: 0s - loss: 9.9296e-05 - mean_absolute_error: 0.0074 81/126 [==================>...........] - ETA: 0s - loss: 1.0026e-04 - mean_absolute_error: 0.0075108/126 [========================>.....] - ETA: 0s - loss: 9.7255e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.7281e-05 - mean_absolute_error: 0.0074 - val_loss: 1.2331e-04 - val_mean_absolute_error: 0.0088
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 8.3532e-05 - mean_absolute_error: 0.0069 27/126 [=====>........................] - ETA: 0s - loss: 1.1627e-04 - mean_absolute_error: 0.0081 53/126 [===========>..................] - ETA: 0s - loss: 1.0951e-04 - mean_absolute_error: 0.0079 78/126 [=================>............] - ETA: 0s - loss: 1.0694e-04 - mean_absolute_error: 0.0078105/126 [========================>.....] - ETA: 0s - loss: 1.0203e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0091e-04 - mean_absolute_error: 0.0076 - val_loss: 1.2393e-04 - val_mean_absolute_error: 0.0089
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 9.2858e-05 - mean_absolute_error: 0.0075 27/126 [=====>........................] - ETA: 0s - loss: 1.0626e-04 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 9.9126e-05 - mean_absolute_error: 0.0075 79/126 [=================>............] - ETA: 0s - loss: 1.0018e-04 - mean_absolute_error: 0.0075105/126 [========================>.....] - ETA: 0s - loss: 9.9503e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.8306e-05 - mean_absolute_error: 0.0075 - val_loss: 1.0782e-04 - val_mean_absolute_error: 0.0081
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 8.2322e-05 - mean_absolute_error: 0.0064 27/126 [=====>........................] - ETA: 0s - loss: 9.1630e-05 - mean_absolute_error: 0.0073 53/126 [===========>..................] - ETA: 0s - loss: 9.5292e-05 - mean_absolute_error: 0.0073 78/126 [=================>............] - ETA: 0s - loss: 9.5387e-05 - mean_absolute_error: 0.0073104/126 [=======================>......] - ETA: 0s - loss: 9.5104e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.7543e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0694e-04 - val_mean_absolute_error: 0.0079
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 7.8156e-05 - mean_absolute_error: 0.0068 28/126 [=====>........................] - ETA: 0s - loss: 1.0070e-04 - mean_absolute_error: 0.0074 54/126 [===========>..................] - ETA: 0s - loss: 9.7387e-05 - mean_absolute_error: 0.0074 80/126 [==================>...........] - ETA: 0s - loss: 9.5205e-05 - mean_absolute_error: 0.0074107/126 [========================>.....] - ETA: 0s - loss: 9.8526e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.7029e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1070e-04 - val_mean_absolute_error: 0.0083
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 7.4919e-05 - mean_absolute_error: 0.0064 28/126 [=====>........................] - ETA: 0s - loss: 1.0063e-04 - mean_absolute_error: 0.0074 55/126 [============>.................] - ETA: 0s - loss: 1.0290e-04 - mean_absolute_error: 0.0076 81/126 [==================>...........] - ETA: 0s - loss: 1.0038e-04 - mean_absolute_error: 0.0076108/126 [========================>.....] - ETA: 0s - loss: 1.0058e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0353e-04 - mean_absolute_error: 0.0078 - val_loss: 1.4556e-04 - val_mean_absolute_error: 0.0097
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 4.4771e-05 - mean_absolute_error: 0.0056 27/126 [=====>........................] - ETA: 0s - loss: 9.4895e-05 - mean_absolute_error: 0.0073 54/126 [===========>..................] - ETA: 0s - loss: 9.8218e-05 - mean_absolute_error: 0.0074 80/126 [==================>...........] - ETA: 0s - loss: 9.8309e-05 - mean_absolute_error: 0.0074107/126 [========================>.....] - ETA: 0s - loss: 9.8456e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7095e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0782e-04 - val_mean_absolute_error: 0.0080
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 6.1078e-05 - mean_absolute_error: 0.0060 27/126 [=====>........................] - ETA: 0s - loss: 9.1058e-05 - mean_absolute_error: 0.0074 53/126 [===========>..................] - ETA: 0s - loss: 9.7172e-05 - mean_absolute_error: 0.0075 79/126 [=================>............] - ETA: 0s - loss: 1.0172e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 1.0148e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0169e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0942e-04 - val_mean_absolute_error: 0.0081
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 9.7368e-05 - mean_absolute_error: 0.0077 27/126 [=====>........................] - ETA: 0s - loss: 1.0144e-04 - mean_absolute_error: 0.0076 53/126 [===========>..................] - ETA: 0s - loss: 1.1043e-04 - mean_absolute_error: 0.0079 80/126 [==================>...........] - ETA: 0s - loss: 1.0743e-04 - mean_absolute_error: 0.0078106/126 [========================>.....] - ETA: 0s - loss: 1.0210e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0129e-04 - mean_absolute_error: 0.0076 - val_loss: 1.2535e-04 - val_mean_absolute_error: 0.0089
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 6.4530e-05 - mean_absolute_error: 0.0070 27/126 [=====>........................] - ETA: 0s - loss: 1.0225e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.0206e-04 - mean_absolute_error: 0.0077 80/126 [==================>...........] - ETA: 0s - loss: 1.0686e-04 - mean_absolute_error: 0.0079106/126 [========================>.....] - ETA: 0s - loss: 1.0917e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0976e-04 - mean_absolute_error: 0.0080 - val_loss: 1.4920e-04 - val_mean_absolute_error: 0.0097
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 1.0914e-04 - mean_absolute_error: 0.0086 28/126 [=====>........................] - ETA: 0s - loss: 9.5592e-05 - mean_absolute_error: 0.0074 55/126 [============>.................] - ETA: 0s - loss: 1.0400e-04 - mean_absolute_error: 0.0077 81/126 [==================>...........] - ETA: 0s - loss: 1.0016e-04 - mean_absolute_error: 0.0076107/126 [========================>.....] - ETA: 0s - loss: 9.9738e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.9035e-05 - mean_absolute_error: 0.0075 - val_loss: 1.0959e-04 - val_mean_absolute_error: 0.0082
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 6.0000e-05 - mean_absolute_error: 0.0066 28/126 [=====>........................] - ETA: 0s - loss: 9.9238e-05 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 9.8038e-05 - mean_absolute_error: 0.0076 81/126 [==================>...........] - ETA: 0s - loss: 9.6079e-05 - mean_absolute_error: 0.0074108/126 [========================>.....] - ETA: 0s - loss: 9.6213e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.7709e-05 - mean_absolute_error: 0.0075 - val_loss: 1.2863e-04 - val_mean_absolute_error: 0.0089
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 5.9155e-05 - mean_absolute_error: 0.0065 27/126 [=====>........................] - ETA: 0s - loss: 1.0517e-04 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 1.0202e-04 - mean_absolute_error: 0.0076 79/126 [=================>............] - ETA: 0s - loss: 9.9025e-05 - mean_absolute_error: 0.0075102/126 [=======================>......] - ETA: 0s - loss: 1.0261e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0151e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0667e-04 - val_mean_absolute_error: 0.0081
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 1.0404e-04 - mean_absolute_error: 0.0090 27/126 [=====>........................] - ETA: 0s - loss: 1.0718e-04 - mean_absolute_error: 0.0080 52/126 [===========>..................] - ETA: 0s - loss: 1.1120e-04 - mean_absolute_error: 0.0080 78/126 [=================>............] - ETA: 0s - loss: 1.0426e-04 - mean_absolute_error: 0.0078104/126 [=======================>......] - ETA: 0s - loss: 1.0175e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0106e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0705e-04 - val_mean_absolute_error: 0.0080
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 4.1481e-05 - mean_absolute_error: 0.0050 27/126 [=====>........................] - ETA: 0s - loss: 1.0193e-04 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 1.0038e-04 - mean_absolute_error: 0.0076 78/126 [=================>............] - ETA: 0s - loss: 1.0484e-04 - mean_absolute_error: 0.0077100/126 [======================>.......] - ETA: 0s - loss: 1.1234e-04 - mean_absolute_error: 0.0080125/126 [============================>.] - ETA: 0s - loss: 1.0957e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0962e-04 - mean_absolute_error: 0.0080 - val_loss: 1.0787e-04 - val_mean_absolute_error: 0.0080
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 1.1282e-04 - mean_absolute_error: 0.0087 27/126 [=====>........................] - ETA: 0s - loss: 9.2659e-05 - mean_absolute_error: 0.0074 53/126 [===========>..................] - ETA: 0s - loss: 9.6800e-05 - mean_absolute_error: 0.0075 79/126 [=================>............] - ETA: 0s - loss: 9.4295e-05 - mean_absolute_error: 0.0074105/126 [========================>.....] - ETA: 0s - loss: 9.7341e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0065e-04 - mean_absolute_error: 0.0077 - val_loss: 1.2189e-04 - val_mean_absolute_error: 0.0086
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 8.5456e-05 - mean_absolute_error: 0.0076 27/126 [=====>........................] - ETA: 0s - loss: 9.5756e-05 - mean_absolute_error: 0.0074 53/126 [===========>..................] - ETA: 0s - loss: 9.7615e-05 - mean_absolute_error: 0.0075 79/126 [=================>............] - ETA: 0s - loss: 9.9355e-05 - mean_absolute_error: 0.0075104/126 [=======================>......] - ETA: 0s - loss: 1.0032e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0192e-04 - mean_absolute_error: 0.0076 - val_loss: 1.1594e-04 - val_mean_absolute_error: 0.0085
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 6.8770e-05 - mean_absolute_error: 0.0064 28/126 [=====>........................] - ETA: 0s - loss: 9.6141e-05 - mean_absolute_error: 0.0076 54/126 [===========>..................] - ETA: 0s - loss: 1.0064e-04 - mean_absolute_error: 0.0077 80/126 [==================>...........] - ETA: 0s - loss: 1.0008e-04 - mean_absolute_error: 0.0076106/126 [========================>.....] - ETA: 0s - loss: 1.0236e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0504e-04 - mean_absolute_error: 0.0078 - val_loss: 1.9488e-04 - val_mean_absolute_error: 0.0115
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 1.3032e-04 - mean_absolute_error: 0.0091 27/126 [=====>........................] - ETA: 0s - loss: 1.0449e-04 - mean_absolute_error: 0.0078 52/126 [===========>..................] - ETA: 0s - loss: 1.0411e-04 - mean_absolute_error: 0.0078 78/126 [=================>............] - ETA: 0s - loss: 1.1211e-04 - mean_absolute_error: 0.0081104/126 [=======================>......] - ETA: 0s - loss: 1.0810e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0854e-04 - mean_absolute_error: 0.0079 - val_loss: 1.0445e-04 - val_mean_absolute_error: 0.0079
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 5.5587e-05 - mean_absolute_error: 0.0057 28/126 [=====>........................] - ETA: 0s - loss: 1.0776e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.0139e-04 - mean_absolute_error: 0.0075 78/126 [=================>............] - ETA: 0s - loss: 1.0522e-04 - mean_absolute_error: 0.0076104/126 [=======================>......] - ETA: 0s - loss: 1.0257e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0062e-04 - mean_absolute_error: 0.0076 - val_loss: 1.4910e-04 - val_mean_absolute_error: 0.0098
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 7.4081e-05 - mean_absolute_error: 0.0068 27/126 [=====>........................] - ETA: 0s - loss: 1.3093e-04 - mean_absolute_error: 0.0085 53/126 [===========>..................] - ETA: 0s - loss: 1.0912e-04 - mean_absolute_error: 0.0079 79/126 [=================>............] - ETA: 0s - loss: 1.0897e-04 - mean_absolute_error: 0.0079106/126 [========================>.....] - ETA: 0s - loss: 1.0624e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0520e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0669e-04 - val_mean_absolute_error: 0.0080
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 9.0052e-05 - mean_absolute_error: 0.0080 27/126 [=====>........................] - ETA: 0s - loss: 9.7213e-05 - mean_absolute_error: 0.0075 53/126 [===========>..................] - ETA: 0s - loss: 1.0197e-04 - mean_absolute_error: 0.0077 79/126 [=================>............] - ETA: 0s - loss: 1.0062e-04 - mean_absolute_error: 0.0077106/126 [========================>.....] - ETA: 0s - loss: 1.0057e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0149e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0498e-04 - val_mean_absolute_error: 0.0078
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 8.5437e-05 - mean_absolute_error: 0.0073 26/126 [=====>........................] - ETA: 0s - loss: 1.1493e-04 - mean_absolute_error: 0.0084 52/126 [===========>..................] - ETA: 0s - loss: 1.1246e-04 - mean_absolute_error: 0.0082 78/126 [=================>............] - ETA: 0s - loss: 1.1217e-04 - mean_absolute_error: 0.0082104/126 [=======================>......] - ETA: 0s - loss: 1.0715e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0600e-04 - mean_absolute_error: 0.0079 - val_loss: 1.0827e-04 - val_mean_absolute_error: 0.0081
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 1.7712e-04 - mean_absolute_error: 0.0092 27/126 [=====>........................] - ETA: 0s - loss: 9.9493e-05 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 1.1049e-04 - mean_absolute_error: 0.0080 78/126 [=================>............] - ETA: 0s - loss: 1.0903e-04 - mean_absolute_error: 0.0079104/126 [=======================>......] - ETA: 0s - loss: 1.0773e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0437e-04 - mean_absolute_error: 0.0078 - val_loss: 1.6504e-04 - val_mean_absolute_error: 0.0103
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 1.6818e-04 - mean_absolute_error: 0.0104 27/126 [=====>........................] - ETA: 0s - loss: 1.1293e-04 - mean_absolute_error: 0.0083 54/126 [===========>..................] - ETA: 0s - loss: 1.2229e-04 - mean_absolute_error: 0.0085 80/126 [==================>...........] - ETA: 0s - loss: 1.2294e-04 - mean_absolute_error: 0.0086105/126 [========================>.....] - ETA: 0s - loss: 1.2164e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 2ms/step - loss: 1.1869e-04 - mean_absolute_error: 0.0084 - val_loss: 1.1371e-04 - val_mean_absolute_error: 0.0084
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 1.3038e-04 - mean_absolute_error: 0.0085 28/126 [=====>........................] - ETA: 0s - loss: 1.1116e-04 - mean_absolute_error: 0.0080 54/126 [===========>..................] - ETA: 0s - loss: 1.0935e-04 - mean_absolute_error: 0.0081 80/126 [==================>...........] - ETA: 0s - loss: 1.1276e-04 - mean_absolute_error: 0.0083106/126 [========================>.....] - ETA: 0s - loss: 1.1166e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1165e-04 - mean_absolute_error: 0.0082 - val_loss: 2.1434e-04 - val_mean_absolute_error: 0.0122
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 2.2281e-04 - mean_absolute_error: 0.0126 23/126 [====>.........................] - ETA: 0s - loss: 1.1008e-04 - mean_absolute_error: 0.0081 45/126 [=========>....................] - ETA: 0s - loss: 1.0238e-04 - mean_absolute_error: 0.0078 65/126 [==============>...............] - ETA: 0s - loss: 1.0564e-04 - mean_absolute_error: 0.0079 87/126 [===================>..........] - ETA: 0s - loss: 1.1280e-04 - mean_absolute_error: 0.0082109/126 [========================>.....] - ETA: 0s - loss: 1.1162e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 3ms/step - loss: 1.1133e-04 - mean_absolute_error: 0.0081 - val_loss: 1.0366e-04 - val_mean_absolute_error: 0.0079
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 1.1566e-04 - mean_absolute_error: 0.0078 27/126 [=====>........................] - ETA: 0s - loss: 9.6350e-05 - mean_absolute_error: 0.0074 54/126 [===========>..................] - ETA: 0s - loss: 1.0476e-04 - mean_absolute_error: 0.0078 81/126 [==================>...........] - ETA: 0s - loss: 1.0592e-04 - mean_absolute_error: 0.0078107/126 [========================>.....] - ETA: 0s - loss: 1.0633e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0693e-04 - mean_absolute_error: 0.0079 - val_loss: 1.1094e-04 - val_mean_absolute_error: 0.0082
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 1.7138e-04 - mean_absolute_error: 0.0101 28/126 [=====>........................] - ETA: 0s - loss: 9.1729e-05 - mean_absolute_error: 0.0073 55/126 [============>.................] - ETA: 0s - loss: 1.0847e-04 - mean_absolute_error: 0.0080 80/126 [==================>...........] - ETA: 0s - loss: 1.2130e-04 - mean_absolute_error: 0.0085107/126 [========================>.....] - ETA: 0s - loss: 1.1757e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.1896e-04 - mean_absolute_error: 0.0084 - val_loss: 3.3178e-04 - val_mean_absolute_error: 0.0159
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 2.5055e-04 - mean_absolute_error: 0.0127 27/126 [=====>........................] - ETA: 0s - loss: 1.4333e-04 - mean_absolute_error: 0.0092 53/126 [===========>..................] - ETA: 0s - loss: 1.2773e-04 - mean_absolute_error: 0.0087 80/126 [==================>...........] - ETA: 0s - loss: 1.1916e-04 - mean_absolute_error: 0.0084107/126 [========================>.....] - ETA: 0s - loss: 1.1145e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.2021e-04 - mean_absolute_error: 0.0084 - val_loss: 2.6749e-04 - val_mean_absolute_error: 0.0140
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 3.1949e-04 - mean_absolute_error: 0.0142 27/126 [=====>........................] - ETA: 0s - loss: 1.2211e-04 - mean_absolute_error: 0.0084 52/126 [===========>..................] - ETA: 0s - loss: 1.0875e-04 - mean_absolute_error: 0.0079 76/126 [=================>............] - ETA: 0s - loss: 1.0751e-04 - mean_absolute_error: 0.0078102/126 [=======================>......] - ETA: 0s - loss: 1.0862e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.1986e-04 - mean_absolute_error: 0.0084 - val_loss: 1.2162e-04 - val_mean_absolute_error: 0.0088
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 9.8410e-05 - mean_absolute_error: 0.0081 27/126 [=====>........................] - ETA: 0s - loss: 9.1629e-05 - mean_absolute_error: 0.0072 53/126 [===========>..................] - ETA: 0s - loss: 9.0516e-05 - mean_absolute_error: 0.0072 78/126 [=================>............] - ETA: 0s - loss: 9.5008e-05 - mean_absolute_error: 0.0074104/126 [=======================>......] - ETA: 0s - loss: 1.0315e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0312e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0185e-04 - val_mean_absolute_error: 0.0078
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 5.1789e-05 - mean_absolute_error: 0.0059 27/126 [=====>........................] - ETA: 0s - loss: 1.0164e-04 - mean_absolute_error: 0.0074 53/126 [===========>..................] - ETA: 0s - loss: 1.1285e-04 - mean_absolute_error: 0.0080 80/126 [==================>...........] - ETA: 0s - loss: 1.0785e-04 - mean_absolute_error: 0.0078106/126 [========================>.....] - ETA: 0s - loss: 1.0705e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0644e-04 - mean_absolute_error: 0.0079 - val_loss: 1.0507e-04 - val_mean_absolute_error: 0.0080
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 1.6485e-04 - mean_absolute_error: 0.0087 28/126 [=====>........................] - ETA: 0s - loss: 1.0180e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.1033e-04 - mean_absolute_error: 0.0079 80/126 [==================>...........] - ETA: 0s - loss: 1.1148e-04 - mean_absolute_error: 0.0081106/126 [========================>.....] - ETA: 0s - loss: 1.0686e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0484e-04 - mean_absolute_error: 0.0078 - val_loss: 1.1981e-04 - val_mean_absolute_error: 0.0087
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 8.8266e-05 - mean_absolute_error: 0.0073 27/126 [=====>........................] - ETA: 0s - loss: 1.1417e-04 - mean_absolute_error: 0.0080 54/126 [===========>..................] - ETA: 0s - loss: 1.1042e-04 - mean_absolute_error: 0.0080 79/126 [=================>............] - ETA: 0s - loss: 1.1599e-04 - mean_absolute_error: 0.0083106/126 [========================>.....] - ETA: 0s - loss: 1.1590e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1382e-04 - mean_absolute_error: 0.0082 - val_loss: 1.6052e-04 - val_mean_absolute_error: 0.0104
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 2.1090e-04 - mean_absolute_error: 0.0108 28/126 [=====>........................] - ETA: 0s - loss: 1.0425e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.0008e-04 - mean_absolute_error: 0.0076 80/126 [==================>...........] - ETA: 0s - loss: 1.0006e-04 - mean_absolute_error: 0.0076106/126 [========================>.....] - ETA: 0s - loss: 9.7262e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 1.0903e-04 - mean_absolute_error: 0.0079 - val_loss: 2.0227e-04 - val_mean_absolute_error: 0.0118
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 1.2693e-04 - mean_absolute_error: 0.0090 28/126 [=====>........................] - ETA: 0s - loss: 1.0861e-04 - mean_absolute_error: 0.0082 55/126 [============>.................] - ETA: 0s - loss: 1.0125e-04 - mean_absolute_error: 0.0077 81/126 [==================>...........] - ETA: 0s - loss: 1.1137e-04 - mean_absolute_error: 0.0081108/126 [========================>.....] - ETA: 0s - loss: 1.1185e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1635e-04 - mean_absolute_error: 0.0083 - val_loss: 1.6007e-04 - val_mean_absolute_error: 0.0101
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 1.2196e-04 - mean_absolute_error: 0.0085 26/126 [=====>........................] - ETA: 0s - loss: 1.3490e-04 - mean_absolute_error: 0.0090 51/126 [===========>..................] - ETA: 0s - loss: 1.1115e-04 - mean_absolute_error: 0.0081 76/126 [=================>............] - ETA: 0s - loss: 1.2008e-04 - mean_absolute_error: 0.0084102/126 [=======================>......] - ETA: 0s - loss: 1.2093e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.2387e-04 - mean_absolute_error: 0.0086 - val_loss: 1.0268e-04 - val_mean_absolute_error: 0.0079
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 6.6942e-05 - mean_absolute_error: 0.0067 28/126 [=====>........................] - ETA: 0s - loss: 1.0190e-04 - mean_absolute_error: 0.0078 54/126 [===========>..................] - ETA: 0s - loss: 9.8680e-05 - mean_absolute_error: 0.0077 79/126 [=================>............] - ETA: 0s - loss: 1.0117e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 1.0001e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0071e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0637e-04 - val_mean_absolute_error: 0.0081
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 8.0851e-05 - mean_absolute_error: 0.0065 27/126 [=====>........................] - ETA: 0s - loss: 1.1319e-04 - mean_absolute_error: 0.0080 53/126 [===========>..................] - ETA: 0s - loss: 1.2058e-04 - mean_absolute_error: 0.0085 79/126 [=================>............] - ETA: 0s - loss: 1.1427e-04 - mean_absolute_error: 0.0082104/126 [=======================>......] - ETA: 0s - loss: 1.0862e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0655e-04 - mean_absolute_error: 0.0079 - val_loss: 1.1047e-04 - val_mean_absolute_error: 0.0082
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 1.1623e-04 - mean_absolute_error: 0.0086 27/126 [=====>........................] - ETA: 0s - loss: 1.2608e-04 - mean_absolute_error: 0.0087 53/126 [===========>..................] - ETA: 0s - loss: 1.2614e-04 - mean_absolute_error: 0.0086 79/126 [=================>............] - ETA: 0s - loss: 1.2245e-04 - mean_absolute_error: 0.0085105/126 [========================>.....] - ETA: 0s - loss: 1.1837e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 2ms/step - loss: 1.1825e-04 - mean_absolute_error: 0.0084 - val_loss: 1.0017e-04 - val_mean_absolute_error: 0.0078
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 5.4694e-05 - mean_absolute_error: 0.0054 27/126 [=====>........................] - ETA: 0s - loss: 1.0525e-04 - mean_absolute_error: 0.0078 54/126 [===========>..................] - ETA: 0s - loss: 1.1856e-04 - mean_absolute_error: 0.0084 80/126 [==================>...........] - ETA: 0s - loss: 1.1496e-04 - mean_absolute_error: 0.0083107/126 [========================>.....] - ETA: 0s - loss: 1.1322e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1232e-04 - mean_absolute_error: 0.0082 - val_loss: 1.7389e-04 - val_mean_absolute_error: 0.0106
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 1.3651e-04 - mean_absolute_error: 0.0101 27/126 [=====>........................] - ETA: 0s - loss: 1.2957e-04 - mean_absolute_error: 0.0087 53/126 [===========>..................] - ETA: 0s - loss: 1.1496e-04 - mean_absolute_error: 0.0081 79/126 [=================>............] - ETA: 0s - loss: 1.0457e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 1.0623e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0666e-04 - mean_absolute_error: 0.0078 - val_loss: 1.2443e-04 - val_mean_absolute_error: 0.0089
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 1.1446e-04 - mean_absolute_error: 0.0087 27/126 [=====>........................] - ETA: 0s - loss: 1.2695e-04 - mean_absolute_error: 0.0087 53/126 [===========>..................] - ETA: 0s - loss: 1.1445e-04 - mean_absolute_error: 0.0082 78/126 [=================>............] - ETA: 0s - loss: 1.1078e-04 - mean_absolute_error: 0.0081104/126 [=======================>......] - ETA: 0s - loss: 1.1202e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1564e-04 - mean_absolute_error: 0.0084 - val_loss: 1.2495e-04 - val_mean_absolute_error: 0.0088
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 9.3338e-05 - mean_absolute_error: 0.0084 25/126 [====>.........................] - ETA: 0s - loss: 1.0487e-04 - mean_absolute_error: 0.0079 51/126 [===========>..................] - ETA: 0s - loss: 1.0767e-04 - mean_absolute_error: 0.0079 77/126 [=================>............] - ETA: 0s - loss: 1.0434e-04 - mean_absolute_error: 0.0078102/126 [=======================>......] - ETA: 0s - loss: 1.0442e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0215e-04 - mean_absolute_error: 0.0077 - val_loss: 1.3352e-04 - val_mean_absolute_error: 0.0093
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 1.6658e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.0999e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.0712e-04 - mean_absolute_error: 0.0078 79/126 [=================>............] - ETA: 0s - loss: 1.0771e-04 - mean_absolute_error: 0.0079105/126 [========================>.....] - ETA: 0s - loss: 1.0531e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0226e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0126e-04 - val_mean_absolute_error: 0.0078
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 4.3649e-05 - mean_absolute_error: 0.0050 28/126 [=====>........................] - ETA: 0s - loss: 1.0150e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.0256e-04 - mean_absolute_error: 0.0077 80/126 [==================>...........] - ETA: 0s - loss: 1.0684e-04 - mean_absolute_error: 0.0080107/126 [========================>.....] - ETA: 0s - loss: 1.1309e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.1371e-04 - mean_absolute_error: 0.0082 - val_loss: 1.7565e-04 - val_mean_absolute_error: 0.0109
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 1.0041e-04 - mean_absolute_error: 0.0081 28/126 [=====>........................] - ETA: 0s - loss: 9.6048e-05 - mean_absolute_error: 0.0076 53/126 [===========>..................] - ETA: 0s - loss: 9.3362e-05 - mean_absolute_error: 0.0074 79/126 [=================>............] - ETA: 0s - loss: 9.6493e-05 - mean_absolute_error: 0.0075104/126 [=======================>......] - ETA: 0s - loss: 1.0205e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0255e-04 - mean_absolute_error: 0.0077 - val_loss: 1.3630e-04 - val_mean_absolute_error: 0.0093
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 8.3400e-05 - mean_absolute_error: 0.0076 26/126 [=====>........................] - ETA: 0s - loss: 1.6530e-04 - mean_absolute_error: 0.0099 53/126 [===========>..................] - ETA: 0s - loss: 1.4975e-04 - mean_absolute_error: 0.0096 80/126 [==================>...........] - ETA: 0s - loss: 1.4148e-04 - mean_absolute_error: 0.0092107/126 [========================>.....] - ETA: 0s - loss: 1.4019e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.3233e-04 - mean_absolute_error: 0.0089 - val_loss: 9.7954e-05 - val_mean_absolute_error: 0.0077
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 7.9586e-05 - mean_absolute_error: 0.0077 28/126 [=====>........................] - ETA: 0s - loss: 9.6270e-05 - mean_absolute_error: 0.0073 53/126 [===========>..................] - ETA: 0s - loss: 9.2368e-05 - mean_absolute_error: 0.0073 79/126 [=================>............] - ETA: 0s - loss: 1.0144e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 9.9157e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0350e-04 - mean_absolute_error: 0.0078 - val_loss: 4.3982e-04 - val_mean_absolute_error: 0.0189
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 3.2530e-04 - mean_absolute_error: 0.0162 28/126 [=====>........................] - ETA: 0s - loss: 1.1898e-04 - mean_absolute_error: 0.0083 54/126 [===========>..................] - ETA: 0s - loss: 1.0411e-04 - mean_absolute_error: 0.0079 80/126 [==================>...........] - ETA: 0s - loss: 1.0499e-04 - mean_absolute_error: 0.0078106/126 [========================>.....] - ETA: 0s - loss: 1.0251e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0272e-04 - mean_absolute_error: 0.0077 - val_loss: 1.3260e-04 - val_mean_absolute_error: 0.0091
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 1.1115e-04 - mean_absolute_error: 0.0092 26/126 [=====>........................] - ETA: 0s - loss: 1.1039e-04 - mean_absolute_error: 0.0081 52/126 [===========>..................] - ETA: 0s - loss: 1.0600e-04 - mean_absolute_error: 0.0078 78/126 [=================>............] - ETA: 0s - loss: 9.7776e-05 - mean_absolute_error: 0.0075104/126 [=======================>......] - ETA: 0s - loss: 9.8018e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0268e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0431e-04 - val_mean_absolute_error: 0.0080
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 7.6648e-05 - mean_absolute_error: 0.0070 28/126 [=====>........................] - ETA: 0s - loss: 1.0094e-04 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 9.5155e-05 - mean_absolute_error: 0.0074 79/126 [=================>............] - ETA: 0s - loss: 1.0419e-04 - mean_absolute_error: 0.0078105/126 [========================>.....] - ETA: 0s - loss: 1.0378e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0160e-04 - mean_absolute_error: 0.0076 - val_loss: 1.1516e-04 - val_mean_absolute_error: 0.0084
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 5.5744e-05 - mean_absolute_error: 0.0057 28/126 [=====>........................] - ETA: 0s - loss: 1.0593e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 9.7270e-05 - mean_absolute_error: 0.0074 81/126 [==================>...........] - ETA: 0s - loss: 9.5358e-05 - mean_absolute_error: 0.0073105/126 [========================>.....] - ETA: 0s - loss: 9.8401e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.6973e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1089e-04 - val_mean_absolute_error: 0.0084
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 7.7922e-05 - mean_absolute_error: 0.0069 27/126 [=====>........................] - ETA: 0s - loss: 2.2678e-04 - mean_absolute_error: 0.0123 54/126 [===========>..................] - ETA: 0s - loss: 1.7329e-04 - mean_absolute_error: 0.0104 80/126 [==================>...........] - ETA: 0s - loss: 1.4937e-04 - mean_absolute_error: 0.0095105/126 [========================>.....] - ETA: 0s - loss: 1.4004e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.3568e-04 - mean_absolute_error: 0.0090 - val_loss: 9.6218e-05 - val_mean_absolute_error: 0.0075
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 1.3604e-04 - mean_absolute_error: 0.0089 27/126 [=====>........................] - ETA: 0s - loss: 1.0568e-04 - mean_absolute_error: 0.0080 53/126 [===========>..................] - ETA: 0s - loss: 1.0130e-04 - mean_absolute_error: 0.0078 78/126 [=================>............] - ETA: 0s - loss: 1.0769e-04 - mean_absolute_error: 0.0080105/126 [========================>.....] - ETA: 0s - loss: 1.0545e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0466e-04 - mean_absolute_error: 0.0078 - val_loss: 9.8381e-05 - val_mean_absolute_error: 0.0078
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 5.7149e-05 - mean_absolute_error: 0.0061 27/126 [=====>........................] - ETA: 0s - loss: 9.1037e-05 - mean_absolute_error: 0.0072 52/126 [===========>..................] - ETA: 0s - loss: 1.0045e-04 - mean_absolute_error: 0.0077 78/126 [=================>............] - ETA: 0s - loss: 1.0003e-04 - mean_absolute_error: 0.0077103/126 [=======================>......] - ETA: 0s - loss: 9.9742e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 9.8654e-05 - mean_absolute_error: 0.0076 - val_loss: 1.2380e-04 - val_mean_absolute_error: 0.0088
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 1.0835e-04 - mean_absolute_error: 0.0088 26/126 [=====>........................] - ETA: 0s - loss: 9.6325e-05 - mean_absolute_error: 0.0073 53/126 [===========>..................] - ETA: 0s - loss: 9.1221e-05 - mean_absolute_error: 0.0071 79/126 [=================>............] - ETA: 0s - loss: 9.2309e-05 - mean_absolute_error: 0.0072105/126 [========================>.....] - ETA: 0s - loss: 9.5694e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.5090e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0624e-04 - val_mean_absolute_error: 0.0081
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 6.2768e-05 - mean_absolute_error: 0.0066 27/126 [=====>........................] - ETA: 0s - loss: 9.7768e-05 - mean_absolute_error: 0.0075 53/126 [===========>..................] - ETA: 0s - loss: 1.0912e-04 - mean_absolute_error: 0.0081 79/126 [=================>............] - ETA: 0s - loss: 1.0390e-04 - mean_absolute_error: 0.0078105/126 [========================>.....] - ETA: 0s - loss: 1.0431e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0087e-04 - mean_absolute_error: 0.0076 - val_loss: 1.3510e-04 - val_mean_absolute_error: 0.0094
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 1.5021e-04 - mean_absolute_error: 0.0102 27/126 [=====>........................] - ETA: 0s - loss: 9.4272e-05 - mean_absolute_error: 0.0076 52/126 [===========>..................] - ETA: 0s - loss: 1.0237e-04 - mean_absolute_error: 0.0077 78/126 [=================>............] - ETA: 0s - loss: 9.7529e-05 - mean_absolute_error: 0.0075103/126 [=======================>......] - ETA: 0s - loss: 9.7141e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7586e-05 - mean_absolute_error: 0.0075 - val_loss: 9.6022e-05 - val_mean_absolute_error: 0.0075
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 1.1555e-04 - mean_absolute_error: 0.0084 27/126 [=====>........................] - ETA: 0s - loss: 1.0461e-04 - mean_absolute_error: 0.0076 52/126 [===========>..................] - ETA: 0s - loss: 9.6073e-05 - mean_absolute_error: 0.0074 77/126 [=================>............] - ETA: 0s - loss: 9.9998e-05 - mean_absolute_error: 0.0076102/126 [=======================>......] - ETA: 0s - loss: 9.7315e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.8164e-05 - mean_absolute_error: 0.0076 - val_loss: 1.0198e-04 - val_mean_absolute_error: 0.0079
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 1.0582e-04 - mean_absolute_error: 0.0087 27/126 [=====>........................] - ETA: 0s - loss: 9.6934e-05 - mean_absolute_error: 0.0076 52/126 [===========>..................] - ETA: 0s - loss: 9.0164e-05 - mean_absolute_error: 0.0072 74/126 [================>.............] - ETA: 0s - loss: 1.0100e-04 - mean_absolute_error: 0.0077100/126 [======================>.......] - ETA: 0s - loss: 1.1206e-04 - mean_absolute_error: 0.0081126/126 [==============================] - ETA: 0s - loss: 1.1455e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1455e-04 - mean_absolute_error: 0.0082 - val_loss: 1.7433e-04 - val_mean_absolute_error: 0.0107
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 1.7275e-04 - mean_absolute_error: 0.0115 26/126 [=====>........................] - ETA: 0s - loss: 1.3220e-04 - mean_absolute_error: 0.0088 51/126 [===========>..................] - ETA: 0s - loss: 1.1924e-04 - mean_absolute_error: 0.0085 77/126 [=================>............] - ETA: 0s - loss: 1.1134e-04 - mean_absolute_error: 0.0082103/126 [=======================>......] - ETA: 0s - loss: 1.0499e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0237e-04 - mean_absolute_error: 0.0077 - val_loss: 9.6051e-05 - val_mean_absolute_error: 0.0077
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 9.7792e-05 - mean_absolute_error: 0.0072 27/126 [=====>........................] - ETA: 0s - loss: 8.6113e-05 - mean_absolute_error: 0.0071 53/126 [===========>..................] - ETA: 0s - loss: 1.0263e-04 - mean_absolute_error: 0.0078 79/126 [=================>............] - ETA: 0s - loss: 1.0063e-04 - mean_absolute_error: 0.0077105/126 [========================>.....] - ETA: 0s - loss: 9.8277e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7044e-05 - mean_absolute_error: 0.0075 - val_loss: 1.0090e-04 - val_mean_absolute_error: 0.0078
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 1.1756e-04 - mean_absolute_error: 0.0092 27/126 [=====>........................] - ETA: 0s - loss: 1.1094e-04 - mean_absolute_error: 0.0083 53/126 [===========>..................] - ETA: 0s - loss: 1.0245e-04 - mean_absolute_error: 0.0078 78/126 [=================>............] - ETA: 0s - loss: 1.0226e-04 - mean_absolute_error: 0.0077103/126 [=======================>......] - ETA: 0s - loss: 1.0124e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0050e-04 - mean_absolute_error: 0.0076 - val_loss: 9.3375e-05 - val_mean_absolute_error: 0.0075
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 6.5946e-05 - mean_absolute_error: 0.0060 21/126 [====>.........................] - ETA: 0s - loss: 1.1915e-04 - mean_absolute_error: 0.0083 42/126 [=========>....................] - ETA: 0s - loss: 1.0794e-04 - mean_absolute_error: 0.0078 66/126 [==============>...............] - ETA: 0s - loss: 1.0146e-04 - mean_absolute_error: 0.0076 90/126 [====================>.........] - ETA: 0s - loss: 1.0755e-04 - mean_absolute_error: 0.0079115/126 [==========================>...] - ETA: 0s - loss: 1.0770e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 3ms/step - loss: 1.0621e-04 - mean_absolute_error: 0.0079 - val_loss: 1.5495e-04 - val_mean_absolute_error: 0.0102
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 7.9418e-05 - mean_absolute_error: 0.0072 26/126 [=====>........................] - ETA: 0s - loss: 1.2973e-04 - mean_absolute_error: 0.0089 46/126 [=========>....................] - ETA: 0s - loss: 1.1778e-04 - mean_absolute_error: 0.0084 69/126 [===============>..............] - ETA: 0s - loss: 1.1645e-04 - mean_absolute_error: 0.0083 94/126 [=====================>........] - ETA: 0s - loss: 1.1531e-04 - mean_absolute_error: 0.0083118/126 [===========================>..] - ETA: 0s - loss: 1.1219e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1359e-04 - mean_absolute_error: 0.0082 - val_loss: 1.1564e-04 - val_mean_absolute_error: 0.0085
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 1.5447e-04 - mean_absolute_error: 0.0093 26/126 [=====>........................] - ETA: 0s - loss: 9.6835e-05 - mean_absolute_error: 0.0075 51/126 [===========>..................] - ETA: 0s - loss: 9.5546e-05 - mean_absolute_error: 0.0074 75/126 [================>.............] - ETA: 0s - loss: 9.6050e-05 - mean_absolute_error: 0.0075 99/126 [======================>.......] - ETA: 0s - loss: 1.1075e-04 - mean_absolute_error: 0.0081124/126 [============================>.] - ETA: 0s - loss: 1.1958e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 2ms/step - loss: 1.1886e-04 - mean_absolute_error: 0.0084 - val_loss: 1.5455e-04 - val_mean_absolute_error: 0.0102
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 1.1959e-04 - mean_absolute_error: 0.0090 26/126 [=====>........................] - ETA: 0s - loss: 1.0529e-04 - mean_absolute_error: 0.0079 51/126 [===========>..................] - ETA: 0s - loss: 1.0884e-04 - mean_absolute_error: 0.0080 76/126 [=================>............] - ETA: 0s - loss: 1.0357e-04 - mean_absolute_error: 0.0078101/126 [=======================>......] - ETA: 0s - loss: 9.9586e-05 - mean_absolute_error: 0.0077126/126 [==============================] - ETA: 0s - loss: 9.5567e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.5567e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1490e-04 - val_mean_absolute_error: 0.0086
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 1.1457e-04 - mean_absolute_error: 0.0087 27/126 [=====>........................] - ETA: 0s - loss: 7.7405e-05 - mean_absolute_error: 0.0067 53/126 [===========>..................] - ETA: 0s - loss: 9.0794e-05 - mean_absolute_error: 0.0071 77/126 [=================>............] - ETA: 0s - loss: 9.3704e-05 - mean_absolute_error: 0.0073102/126 [=======================>......] - ETA: 0s - loss: 9.6952e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 1.0889e-04 - mean_absolute_error: 0.0080 - val_loss: 9.2151e-05 - val_mean_absolute_error: 0.0074
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 3.8533e-05 - mean_absolute_error: 0.0046 27/126 [=====>........................] - ETA: 0s - loss: 1.0237e-04 - mean_absolute_error: 0.0077 53/126 [===========>..................] - ETA: 0s - loss: 1.0857e-04 - mean_absolute_error: 0.0080 79/126 [=================>............] - ETA: 0s - loss: 1.0495e-04 - mean_absolute_error: 0.0079104/126 [=======================>......] - ETA: 0s - loss: 1.0246e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0027e-04 - mean_absolute_error: 0.0077 - val_loss: 1.1575e-04 - val_mean_absolute_error: 0.0085
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 9.1007e-05 - mean_absolute_error: 0.0077 26/126 [=====>........................] - ETA: 0s - loss: 1.2476e-04 - mean_absolute_error: 0.0086 51/126 [===========>..................] - ETA: 0s - loss: 1.0923e-04 - mean_absolute_error: 0.0080 76/126 [=================>............] - ETA: 0s - loss: 1.0771e-04 - mean_absolute_error: 0.0079101/126 [=======================>......] - ETA: 0s - loss: 1.0392e-04 - mean_absolute_error: 0.0078126/126 [==============================] - ETA: 0s - loss: 1.0043e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0043e-04 - mean_absolute_error: 0.0076 - val_loss: 1.2120e-04 - val_mean_absolute_error: 0.0087
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 7.9309e-05 - mean_absolute_error: 0.0065 26/126 [=====>........................] - ETA: 0s - loss: 7.6612e-05 - mean_absolute_error: 0.0068 51/126 [===========>..................] - ETA: 0s - loss: 8.5717e-05 - mean_absolute_error: 0.0071 73/126 [================>.............] - ETA: 0s - loss: 9.3440e-05 - mean_absolute_error: 0.0073 95/126 [=====================>........] - ETA: 0s - loss: 9.3392e-05 - mean_absolute_error: 0.0074120/126 [===========================>..] - ETA: 0s - loss: 9.5968e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.6393e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1137e-04 - val_mean_absolute_error: 0.0084
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 1.2859e-04 - mean_absolute_error: 0.0096 27/126 [=====>........................] - ETA: 0s - loss: 1.0752e-04 - mean_absolute_error: 0.0081 52/126 [===========>..................] - ETA: 0s - loss: 1.0337e-04 - mean_absolute_error: 0.0078 75/126 [================>.............] - ETA: 0s - loss: 1.0077e-04 - mean_absolute_error: 0.0077 97/126 [======================>.......] - ETA: 0s - loss: 9.8991e-05 - mean_absolute_error: 0.0076118/126 [===========================>..] - ETA: 0s - loss: 9.6541e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.5357e-05 - mean_absolute_error: 0.0075 - val_loss: 1.3702e-04 - val_mean_absolute_error: 0.0093
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 1.2235e-04 - mean_absolute_error: 0.0076 25/126 [====>.........................] - ETA: 0s - loss: 1.0747e-04 - mean_absolute_error: 0.0080 49/126 [==========>...................] - ETA: 0s - loss: 1.1029e-04 - mean_absolute_error: 0.0082 72/126 [================>.............] - ETA: 0s - loss: 1.0544e-04 - mean_absolute_error: 0.0079 97/126 [======================>.......] - ETA: 0s - loss: 1.0156e-04 - mean_absolute_error: 0.0078122/126 [============================>.] - ETA: 0s - loss: 1.0308e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0281e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0277e-04 - val_mean_absolute_error: 0.0080
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 6.3111e-05 - mean_absolute_error: 0.0066 27/126 [=====>........................] - ETA: 0s - loss: 9.7721e-05 - mean_absolute_error: 0.0076 45/126 [=========>....................] - ETA: 0s - loss: 9.7642e-05 - mean_absolute_error: 0.0074 57/126 [============>.................] - ETA: 0s - loss: 9.9189e-05 - mean_absolute_error: 0.0075 76/126 [=================>............] - ETA: 0s - loss: 9.7021e-05 - mean_absolute_error: 0.0075 96/126 [=====================>........] - ETA: 0s - loss: 9.5579e-05 - mean_absolute_error: 0.0075115/126 [==========================>...] - ETA: 0s - loss: 9.7065e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 3ms/step - loss: 9.7292e-05 - mean_absolute_error: 0.0076 - val_loss: 1.5025e-04 - val_mean_absolute_error: 0.0101
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 1.2734e-04 - mean_absolute_error: 0.0090 22/126 [====>.........................] - ETA: 0s - loss: 9.5056e-05 - mean_absolute_error: 0.0076 43/126 [=========>....................] - ETA: 0s - loss: 9.1737e-05 - mean_absolute_error: 0.0075 64/126 [==============>...............] - ETA: 0s - loss: 9.0191e-05 - mean_absolute_error: 0.0075 84/126 [===================>..........] - ETA: 0s - loss: 8.7415e-05 - mean_absolute_error: 0.0073103/126 [=======================>......] - ETA: 0s - loss: 8.7979e-05 - mean_absolute_error: 0.0072124/126 [============================>.] - ETA: 0s - loss: 9.2342e-05 - mean_absolute_error: 0.0073126/126 [==============================] - 0s 3ms/step - loss: 9.2604e-05 - mean_absolute_error: 0.0074 - val_loss: 9.8362e-05 - val_mean_absolute_error: 0.0078
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 7.6331e-05 - mean_absolute_error: 0.0066 23/126 [====>.........................] - ETA: 0s - loss: 9.9070e-05 - mean_absolute_error: 0.0075 43/126 [=========>....................] - ETA: 0s - loss: 9.4168e-05 - mean_absolute_error: 0.0073 63/126 [==============>...............] - ETA: 0s - loss: 8.9151e-05 - mean_absolute_error: 0.0072 83/126 [==================>...........] - ETA: 0s - loss: 8.8621e-05 - mean_absolute_error: 0.0072107/126 [========================>.....] - ETA: 0s - loss: 8.9537e-05 - mean_absolute_error: 0.0071126/126 [==============================] - 0s 3ms/step - loss: 8.8476e-05 - mean_absolute_error: 0.0071 - val_loss: 1.4293e-04 - val_mean_absolute_error: 0.0095
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 6.8785e-05 - mean_absolute_error: 0.0070 26/126 [=====>........................] - ETA: 0s - loss: 1.0158e-04 - mean_absolute_error: 0.0077 52/126 [===========>..................] - ETA: 0s - loss: 9.6409e-05 - mean_absolute_error: 0.0076 74/126 [================>.............] - ETA: 0s - loss: 9.7052e-05 - mean_absolute_error: 0.0076 98/126 [======================>.......] - ETA: 0s - loss: 1.0391e-04 - mean_absolute_error: 0.0079120/126 [===========================>..] - ETA: 0s - loss: 1.0819e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0689e-04 - mean_absolute_error: 0.0080 - val_loss: 9.0818e-05 - val_mean_absolute_error: 0.0073
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 4.6522e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 8.4643e-05 - mean_absolute_error: 0.0070 48/126 [==========>...................] - ETA: 0s - loss: 1.0443e-04 - mean_absolute_error: 0.0078 71/126 [===============>..............] - ETA: 0s - loss: 1.0287e-04 - mean_absolute_error: 0.0079 95/126 [=====================>........] - ETA: 0s - loss: 1.0776e-04 - mean_absolute_error: 0.0081119/126 [===========================>..] - ETA: 0s - loss: 1.0265e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 3ms/step - loss: 1.0291e-04 - mean_absolute_error: 0.0078 - val_loss: 8.8678e-05 - val_mean_absolute_error: 0.0072
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 5.5668e-05 - mean_absolute_error: 0.0059 27/126 [=====>........................] - ETA: 0s - loss: 9.1612e-05 - mean_absolute_error: 0.0076 53/126 [===========>..................] - ETA: 0s - loss: 9.3765e-05 - mean_absolute_error: 0.0075 78/126 [=================>............] - ETA: 0s - loss: 9.8801e-05 - mean_absolute_error: 0.0077104/126 [=======================>......] - ETA: 0s - loss: 1.0744e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0957e-04 - mean_absolute_error: 0.0081 - val_loss: 8.8569e-05 - val_mean_absolute_error: 0.0071
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 8.9583e-05 - mean_absolute_error: 0.0077 13/126 [==>...........................] - ETA: 0s - loss: 8.9402e-05 - mean_absolute_error: 0.0070 26/126 [=====>........................] - ETA: 0s - loss: 8.9175e-05 - mean_absolute_error: 0.0071 42/126 [=========>....................] - ETA: 0s - loss: 8.4963e-05 - mean_absolute_error: 0.0070 53/126 [===========>..................] - ETA: 0s - loss: 8.7078e-05 - mean_absolute_error: 0.0071 65/126 [==============>...............] - ETA: 0s - loss: 8.6686e-05 - mean_absolute_error: 0.0070 79/126 [=================>............] - ETA: 0s - loss: 8.8790e-05 - mean_absolute_error: 0.0071 97/126 [======================>.......] - ETA: 0s - loss: 9.2265e-05 - mean_absolute_error: 0.0073112/126 [=========================>....] - ETA: 0s - loss: 9.7601e-05 - mean_absolute_error: 0.0075126/126 [==============================] - ETA: 0s - loss: 9.7456e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 1s 4ms/step - loss: 9.7456e-05 - mean_absolute_error: 0.0076 - val_loss: 9.3675e-05 - val_mean_absolute_error: 0.0075
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 7.6112e-05 - mean_absolute_error: 0.0074 15/126 [==>...........................] - ETA: 0s - loss: 8.3316e-05 - mean_absolute_error: 0.0069 27/126 [=====>........................] - ETA: 0s - loss: 7.9301e-05 - mean_absolute_error: 0.0067 41/126 [========>.....................] - ETA: 0s - loss: 8.5341e-05 - mean_absolute_error: 0.0071 60/126 [=============>................] - ETA: 0s - loss: 8.4322e-05 - mean_absolute_error: 0.0070 76/126 [=================>............] - ETA: 0s - loss: 8.7896e-05 - mean_absolute_error: 0.0072 94/126 [=====================>........] - ETA: 0s - loss: 9.5768e-05 - mean_absolute_error: 0.0075114/126 [==========================>...] - ETA: 0s - loss: 9.7991e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 4ms/step - loss: 9.8117e-05 - mean_absolute_error: 0.0076 - val_loss: 8.9997e-05 - val_mean_absolute_error: 0.0074
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 1.1795e-04 - mean_absolute_error: 0.0079 17/126 [===>..........................] - ETA: 0s - loss: 9.4726e-05 - mean_absolute_error: 0.0073 34/126 [=======>......................] - ETA: 0s - loss: 9.0487e-05 - mean_absolute_error: 0.0073 49/126 [==========>...................] - ETA: 0s - loss: 9.6355e-05 - mean_absolute_error: 0.0075 65/126 [==============>...............] - ETA: 0s - loss: 9.6088e-05 - mean_absolute_error: 0.0075 83/126 [==================>...........] - ETA: 0s - loss: 9.1472e-05 - mean_absolute_error: 0.0073102/126 [=======================>......] - ETA: 0s - loss: 9.0665e-05 - mean_absolute_error: 0.0072124/126 [============================>.] - ETA: 0s - loss: 9.4308e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 3ms/step - loss: 9.4498e-05 - mean_absolute_error: 0.0074 - val_loss: 9.5112e-05 - val_mean_absolute_error: 0.0076
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 1.1326e-04 - mean_absolute_error: 0.0080 20/126 [===>..........................] - ETA: 0s - loss: 8.3027e-05 - mean_absolute_error: 0.0070 38/126 [========>.....................] - ETA: 0s - loss: 9.0819e-05 - mean_absolute_error: 0.0073 59/126 [=============>................] - ETA: 0s - loss: 9.0475e-05 - mean_absolute_error: 0.0073 79/126 [=================>............] - ETA: 0s - loss: 9.8858e-05 - mean_absolute_error: 0.0076100/126 [======================>.......] - ETA: 0s - loss: 9.6257e-05 - mean_absolute_error: 0.0075120/126 [===========================>..] - ETA: 0s - loss: 9.4880e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 3ms/step - loss: 9.4725e-05 - mean_absolute_error: 0.0074 - val_loss: 8.6883e-05 - val_mean_absolute_error: 0.0071
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 7.2046e-05 - mean_absolute_error: 0.0063 20/126 [===>..........................] - ETA: 0s - loss: 9.0464e-05 - mean_absolute_error: 0.0075 41/126 [========>.....................] - ETA: 0s - loss: 8.1023e-05 - mean_absolute_error: 0.0070 60/126 [=============>................] - ETA: 0s - loss: 7.9333e-05 - mean_absolute_error: 0.0068 80/126 [==================>...........] - ETA: 0s - loss: 8.3059e-05 - mean_absolute_error: 0.0069100/126 [======================>.......] - ETA: 0s - loss: 8.6048e-05 - mean_absolute_error: 0.0071119/126 [===========================>..] - ETA: 0s - loss: 8.6273e-05 - mean_absolute_error: 0.0071126/126 [==============================] - 0s 3ms/step - loss: 8.6586e-05 - mean_absolute_error: 0.0071 - val_loss: 9.5146e-05 - val_mean_absolute_error: 0.0077

<keras.src.callbacks.History at 0x267d6d409d0>

After the training and fitting of the Machine Learning model for Canada, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Austrailia with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
canada_train_pred = canada_model.predict(X_canada_train).flatten()

plt.plot(dates_canada_train, canada_train_pred, linewidth=4)
plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Training Set Predictions vs. Observations")
plt.show()

canada_val_pred = canada_model.predict(X_canada_val).flatten()

plt.plot(dates_canada_val, canada_val_pred, linewidth=4)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Validation Set Predictions vs. Observations")
plt.show()

canada_test_pred = canada_model.predict(X_canada_test).flatten()

plt.plot(dates_canada_test, canada_test_pred, linewidth=4)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 51s 34/126 [=======>......................] - ETA: 0s  67/126 [==============>...............] - ETA: 0s107/126 [========================>.....] - ETA: 0s126/126 [==============================] - 1s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 2ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Canada’s international currency rate with the United States.

```{python}
# Plotting Canada's observational (reference) data with the predictions of its 
# Machine Learning Model (as a way to visually inspect the effectiveness of the 
# model) 
plt.plot(dates_canada_train, canada_train_pred, linewidth=4)
plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.plot(dates_canada_val, canada_val_pred, linewidth=4)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.plot(dates_canada_test, canada_test_pred, linewidth=4)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"], loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations")
plt.show()
```

Then, I worked on the United Kingdom’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split the United Kingdom’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting United Kingdom's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_united_kingdom_train, X_united_kingdom_train, y_united_kingdom_train = dates_united_kingdom[:percentile_80], X_united_kingdom[:percentile_80], y_united_kingdom[:percentile_80]
dates_united_kingdom_val, X_united_kingdom_val, y_united_kingdom_val = dates_united_kingdom[percentile_80:percentile_90], X_united_kingdom[percentile_80:percentile_90], y_united_kingdom[percentile_80:percentile_90]
dates_united_kingdom_test, X_united_kingdom_test, y_united_kingdom_test = dates_united_kingdom[percentile_90:], X_united_kingdom[percentile_90:], y_united_kingdom[percentile_90:]

plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of United Kingdom's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for United Kingdom
united_kingdom_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

united_kingdom_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

united_kingdom_model.fit(X_united_kingdom_train, y_united_kingdom_train, validation_data=(X_united_kingdom_val, y_united_kingdom_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 4:27 - loss: 0.3288 - mean_absolute_error: 0.5718 20/126 [===>..........................] - ETA: 0s - loss: 0.1401 - mean_absolute_error: 0.3398   41/126 [========>.....................] - ETA: 0s - loss: 0.0714 - mean_absolute_error: 0.1992 64/126 [==============>...............] - ETA: 0s - loss: 0.0463 - mean_absolute_error: 0.1391 86/126 [===================>..........] - ETA: 0s - loss: 0.0347 - mean_absolute_error: 0.1091109/126 [========================>.....] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0902126/126 [==============================] - 3s 6ms/step - loss: 0.0240 - mean_absolute_error: 0.0810 - val_loss: 0.0038 - val_mean_absolute_error: 0.0595
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 5.6289e-04 - mean_absolute_error: 0.0200 27/126 [=====>........................] - ETA: 0s - loss: 5.5229e-04 - mean_absolute_error: 0.0192 53/126 [===========>..................] - ETA: 0s - loss: 5.1817e-04 - mean_absolute_error: 0.0185 78/126 [=================>............] - ETA: 0s - loss: 5.0366e-04 - mean_absolute_error: 0.0182104/126 [=======================>......] - ETA: 0s - loss: 4.9967e-04 - mean_absolute_error: 0.0181126/126 [==============================] - 0s 2ms/step - loss: 4.9312e-04 - mean_absolute_error: 0.0181 - val_loss: 0.0024 - val_mean_absolute_error: 0.0465
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 5.0665e-04 - mean_absolute_error: 0.0191 27/126 [=====>........................] - ETA: 0s - loss: 3.9114e-04 - mean_absolute_error: 0.0160 53/126 [===========>..................] - ETA: 0s - loss: 3.7592e-04 - mean_absolute_error: 0.0158 77/126 [=================>............] - ETA: 0s - loss: 3.6572e-04 - mean_absolute_error: 0.0156 99/126 [======================>.......] - ETA: 0s - loss: 3.5448e-04 - mean_absolute_error: 0.0153123/126 [============================>.] - ETA: 0s - loss: 3.4381e-04 - mean_absolute_error: 0.0150126/126 [==============================] - 0s 2ms/step - loss: 3.4299e-04 - mean_absolute_error: 0.0150 - val_loss: 0.0016 - val_mean_absolute_error: 0.0382
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 2.4057e-04 - mean_absolute_error: 0.0134 27/126 [=====>........................] - ETA: 0s - loss: 2.5882e-04 - mean_absolute_error: 0.0132 51/126 [===========>..................] - ETA: 0s - loss: 2.4051e-04 - mean_absolute_error: 0.0126 74/126 [================>.............] - ETA: 0s - loss: 2.2463e-04 - mean_absolute_error: 0.0122 94/126 [=====================>........] - ETA: 0s - loss: 2.1665e-04 - mean_absolute_error: 0.0119118/126 [===========================>..] - ETA: 0s - loss: 2.0105e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 1.9849e-04 - mean_absolute_error: 0.0113 - val_loss: 6.4974e-04 - val_mean_absolute_error: 0.0237
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 1.1111e-04 - mean_absolute_error: 0.0083 26/126 [=====>........................] - ETA: 0s - loss: 1.2213e-04 - mean_absolute_error: 0.0088 51/126 [===========>..................] - ETA: 0s - loss: 1.2493e-04 - mean_absolute_error: 0.0089 75/126 [================>.............] - ETA: 0s - loss: 1.1486e-04 - mean_absolute_error: 0.0085 97/126 [======================>.......] - ETA: 0s - loss: 1.0794e-04 - mean_absolute_error: 0.0082118/126 [===========================>..] - ETA: 0s - loss: 1.0238e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0072e-04 - mean_absolute_error: 0.0079 - val_loss: 1.5406e-04 - val_mean_absolute_error: 0.0102
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 1.0121e-04 - mean_absolute_error: 0.0075 27/126 [=====>........................] - ETA: 0s - loss: 7.0101e-05 - mean_absolute_error: 0.0066 52/126 [===========>..................] - ETA: 0s - loss: 6.3290e-05 - mean_absolute_error: 0.0063 77/126 [=================>............] - ETA: 0s - loss: 6.0956e-05 - mean_absolute_error: 0.0061100/126 [======================>.......] - ETA: 0s - loss: 5.7887e-05 - mean_absolute_error: 0.0059123/126 [============================>.] - ETA: 0s - loss: 5.4573e-05 - mean_absolute_error: 0.0057126/126 [==============================] - 0s 2ms/step - loss: 5.4241e-05 - mean_absolute_error: 0.0057 - val_loss: 6.5314e-05 - val_mean_absolute_error: 0.0056
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 4.7164e-05 - mean_absolute_error: 0.0055 25/126 [====>.........................] - ETA: 0s - loss: 3.9847e-05 - mean_absolute_error: 0.0049 48/126 [==========>...................] - ETA: 0s - loss: 4.0979e-05 - mean_absolute_error: 0.0048 69/126 [===============>..............] - ETA: 0s - loss: 3.9198e-05 - mean_absolute_error: 0.0047 92/126 [====================>.........] - ETA: 0s - loss: 3.8765e-05 - mean_absolute_error: 0.0046116/126 [==========================>...] - ETA: 0s - loss: 3.7670e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7453e-05 - mean_absolute_error: 0.0046 - val_loss: 6.6553e-05 - val_mean_absolute_error: 0.0056
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 2.1743e-05 - mean_absolute_error: 0.0035 27/126 [=====>........................] - ETA: 0s - loss: 3.5981e-05 - mean_absolute_error: 0.0044 51/126 [===========>..................] - ETA: 0s - loss: 3.4949e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.4016e-05 - mean_absolute_error: 0.0043101/126 [=======================>......] - ETA: 0s - loss: 3.4356e-05 - mean_absolute_error: 0.0043126/126 [==============================] - ETA: 0s - loss: 3.5055e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5055e-05 - mean_absolute_error: 0.0043 - val_loss: 7.7194e-05 - val_mean_absolute_error: 0.0066
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 3.4062e-05 - mean_absolute_error: 0.0044 26/126 [=====>........................] - ETA: 0s - loss: 3.3036e-05 - mean_absolute_error: 0.0042 44/126 [=========>....................] - ETA: 0s - loss: 3.4489e-05 - mean_absolute_error: 0.0042 62/126 [=============>................] - ETA: 0s - loss: 3.2765e-05 - mean_absolute_error: 0.0040 81/126 [==================>...........] - ETA: 0s - loss: 3.3773e-05 - mean_absolute_error: 0.0041101/126 [=======================>......] - ETA: 0s - loss: 3.4207e-05 - mean_absolute_error: 0.0041119/126 [===========================>..] - ETA: 0s - loss: 3.4363e-05 - mean_absolute_error: 0.0041126/126 [==============================] - 0s 3ms/step - loss: 3.4108e-05 - mean_absolute_error: 0.0041 - val_loss: 7.6590e-05 - val_mean_absolute_error: 0.0065
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 2.5188e-05 - mean_absolute_error: 0.0037 18/126 [===>..........................] - ETA: 0s - loss: 4.3135e-05 - mean_absolute_error: 0.0046 35/126 [=======>......................] - ETA: 0s - loss: 3.7329e-05 - mean_absolute_error: 0.0043 51/126 [===========>..................] - ETA: 0s - loss: 3.5341e-05 - mean_absolute_error: 0.0042 71/126 [===============>..............] - ETA: 0s - loss: 3.4524e-05 - mean_absolute_error: 0.0042 91/126 [====================>.........] - ETA: 0s - loss: 3.4774e-05 - mean_absolute_error: 0.0042107/126 [========================>.....] - ETA: 0s - loss: 3.4576e-05 - mean_absolute_error: 0.0042126/126 [==============================] - ETA: 0s - loss: 3.4294e-05 - mean_absolute_error: 0.0042126/126 [==============================] - 0s 3ms/step - loss: 3.4294e-05 - mean_absolute_error: 0.0042 - val_loss: 6.9848e-05 - val_mean_absolute_error: 0.0060
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 1.8250e-05 - mean_absolute_error: 0.0030 22/126 [====>.........................] - ETA: 0s - loss: 3.6075e-05 - mean_absolute_error: 0.0043 45/126 [=========>....................] - ETA: 0s - loss: 3.4311e-05 - mean_absolute_error: 0.0042 68/126 [===============>..............] - ETA: 0s - loss: 3.4383e-05 - mean_absolute_error: 0.0042 93/126 [=====================>........] - ETA: 0s - loss: 3.5158e-05 - mean_absolute_error: 0.0043118/126 [===========================>..] - ETA: 0s - loss: 3.6090e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6424e-05 - mean_absolute_error: 0.0044 - val_loss: 6.8823e-05 - val_mean_absolute_error: 0.0059
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 2.5485e-05 - mean_absolute_error: 0.0037 18/126 [===>..........................] - ETA: 0s - loss: 3.9053e-05 - mean_absolute_error: 0.0048 41/126 [========>.....................] - ETA: 0s - loss: 3.6156e-05 - mean_absolute_error: 0.0045 65/126 [==============>...............] - ETA: 0s - loss: 3.4928e-05 - mean_absolute_error: 0.0044 86/126 [===================>..........] - ETA: 0s - loss: 3.4065e-05 - mean_absolute_error: 0.0043108/126 [========================>.....] - ETA: 0s - loss: 3.4601e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.5240e-05 - mean_absolute_error: 0.0044 - val_loss: 8.8484e-05 - val_mean_absolute_error: 0.0073
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 6.1319e-05 - mean_absolute_error: 0.0062 27/126 [=====>........................] - ETA: 0s - loss: 3.8312e-05 - mean_absolute_error: 0.0044 52/126 [===========>..................] - ETA: 0s - loss: 3.5017e-05 - mean_absolute_error: 0.0042 76/126 [=================>............] - ETA: 0s - loss: 3.4219e-05 - mean_absolute_error: 0.0042101/126 [=======================>......] - ETA: 0s - loss: 3.4430e-05 - mean_absolute_error: 0.0042126/126 [==============================] - ETA: 0s - loss: 3.4427e-05 - mean_absolute_error: 0.0042126/126 [==============================] - 0s 2ms/step - loss: 3.4427e-05 - mean_absolute_error: 0.0042 - val_loss: 6.8254e-05 - val_mean_absolute_error: 0.0058
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 3.8796e-05 - mean_absolute_error: 0.0047 26/126 [=====>........................] - ETA: 0s - loss: 3.5678e-05 - mean_absolute_error: 0.0043 50/126 [==========>...................] - ETA: 0s - loss: 3.6094e-05 - mean_absolute_error: 0.0044 75/126 [================>.............] - ETA: 0s - loss: 3.7314e-05 - mean_absolute_error: 0.0045 99/126 [======================>.......] - ETA: 0s - loss: 3.6347e-05 - mean_absolute_error: 0.0044124/126 [============================>.] - ETA: 0s - loss: 3.5178e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5361e-05 - mean_absolute_error: 0.0043 - val_loss: 7.2959e-05 - val_mean_absolute_error: 0.0062
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 2.6958e-05 - mean_absolute_error: 0.0038 27/126 [=====>........................] - ETA: 0s - loss: 3.3950e-05 - mean_absolute_error: 0.0042 52/126 [===========>..................] - ETA: 0s - loss: 3.6129e-05 - mean_absolute_error: 0.0044 77/126 [=================>............] - ETA: 0s - loss: 3.6199e-05 - mean_absolute_error: 0.0043102/126 [=======================>......] - ETA: 0s - loss: 3.6105e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5621e-05 - mean_absolute_error: 0.0043 - val_loss: 6.8231e-05 - val_mean_absolute_error: 0.0058
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 2.8670e-05 - mean_absolute_error: 0.0040 27/126 [=====>........................] - ETA: 0s - loss: 3.6688e-05 - mean_absolute_error: 0.0045 53/126 [===========>..................] - ETA: 0s - loss: 3.5408e-05 - mean_absolute_error: 0.0043 78/126 [=================>............] - ETA: 0s - loss: 3.5316e-05 - mean_absolute_error: 0.0043103/126 [=======================>......] - ETA: 0s - loss: 3.5097e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5555e-05 - mean_absolute_error: 0.0043 - val_loss: 7.2282e-05 - val_mean_absolute_error: 0.0062
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 2.7917e-05 - mean_absolute_error: 0.0037 26/126 [=====>........................] - ETA: 0s - loss: 3.7910e-05 - mean_absolute_error: 0.0044 51/126 [===========>..................] - ETA: 0s - loss: 3.7827e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 3.6963e-05 - mean_absolute_error: 0.0045101/126 [=======================>......] - ETA: 0s - loss: 3.5838e-05 - mean_absolute_error: 0.0043126/126 [==============================] - ETA: 0s - loss: 3.5937e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.5937e-05 - mean_absolute_error: 0.0044 - val_loss: 8.0358e-05 - val_mean_absolute_error: 0.0065
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 4.6587e-05 - mean_absolute_error: 0.0058 26/126 [=====>........................] - ETA: 0s - loss: 4.0168e-05 - mean_absolute_error: 0.0047 52/126 [===========>..................] - ETA: 0s - loss: 4.1235e-05 - mean_absolute_error: 0.0048 77/126 [=================>............] - ETA: 0s - loss: 3.8723e-05 - mean_absolute_error: 0.0045101/126 [=======================>......] - ETA: 0s - loss: 3.8443e-05 - mean_absolute_error: 0.0045126/126 [==============================] - ETA: 0s - loss: 3.6879e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6879e-05 - mean_absolute_error: 0.0044 - val_loss: 7.2107e-05 - val_mean_absolute_error: 0.0062
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 3.8376e-05 - mean_absolute_error: 0.0042 24/126 [====>.........................] - ETA: 0s - loss: 3.9782e-05 - mean_absolute_error: 0.0044 49/126 [==========>...................] - ETA: 0s - loss: 3.7505e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.6971e-05 - mean_absolute_error: 0.0044 99/126 [======================>.......] - ETA: 0s - loss: 3.8476e-05 - mean_absolute_error: 0.0046125/126 [============================>.] - ETA: 0s - loss: 3.6710e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.6640e-05 - mean_absolute_error: 0.0045 - val_loss: 8.0208e-05 - val_mean_absolute_error: 0.0065
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 8.8428e-05 - mean_absolute_error: 0.0079 26/126 [=====>........................] - ETA: 0s - loss: 3.5823e-05 - mean_absolute_error: 0.0043 52/126 [===========>..................] - ETA: 0s - loss: 3.5305e-05 - mean_absolute_error: 0.0043 72/126 [================>.............] - ETA: 0s - loss: 3.4968e-05 - mean_absolute_error: 0.0043 90/126 [====================>.........] - ETA: 0s - loss: 3.4906e-05 - mean_absolute_error: 0.0043111/126 [=========================>....] - ETA: 0s - loss: 3.4843e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.5738e-05 - mean_absolute_error: 0.0043 - val_loss: 6.7708e-05 - val_mean_absolute_error: 0.0057
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 4.3404e-05 - mean_absolute_error: 0.0050 26/126 [=====>........................] - ETA: 0s - loss: 4.1105e-05 - mean_absolute_error: 0.0047 50/126 [==========>...................] - ETA: 0s - loss: 3.9039e-05 - mean_absolute_error: 0.0046 73/126 [================>.............] - ETA: 0s - loss: 3.7402e-05 - mean_absolute_error: 0.0046 94/126 [=====================>........] - ETA: 0s - loss: 3.9218e-05 - mean_absolute_error: 0.0047116/126 [==========================>...] - ETA: 0s - loss: 4.0964e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9654e-05 - mean_absolute_error: 0.0047 - val_loss: 6.6899e-05 - val_mean_absolute_error: 0.0057
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 2.6094e-05 - mean_absolute_error: 0.0040 25/126 [====>.........................] - ETA: 0s - loss: 3.4849e-05 - mean_absolute_error: 0.0044 46/126 [=========>....................] - ETA: 0s - loss: 3.5834e-05 - mean_absolute_error: 0.0043 68/126 [===============>..............] - ETA: 0s - loss: 3.5568e-05 - mean_absolute_error: 0.0043 90/126 [====================>.........] - ETA: 0s - loss: 3.5425e-05 - mean_absolute_error: 0.0044112/126 [=========================>....] - ETA: 0s - loss: 3.5633e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5634e-05 - mean_absolute_error: 0.0044 - val_loss: 1.1426e-04 - val_mean_absolute_error: 0.0087
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 4.7985e-05 - mean_absolute_error: 0.0054 25/126 [====>.........................] - ETA: 0s - loss: 4.2028e-05 - mean_absolute_error: 0.0045 49/126 [==========>...................] - ETA: 0s - loss: 3.8826e-05 - mean_absolute_error: 0.0044 73/126 [================>.............] - ETA: 0s - loss: 4.0485e-05 - mean_absolute_error: 0.0046 98/126 [======================>.......] - ETA: 0s - loss: 3.9329e-05 - mean_absolute_error: 0.0046124/126 [============================>.] - ETA: 0s - loss: 3.8378e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8253e-05 - mean_absolute_error: 0.0046 - val_loss: 8.5120e-05 - val_mean_absolute_error: 0.0071
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 2.5044e-05 - mean_absolute_error: 0.0039 27/126 [=====>........................] - ETA: 0s - loss: 3.7736e-05 - mean_absolute_error: 0.0045 52/126 [===========>..................] - ETA: 0s - loss: 3.9386e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 4.3386e-05 - mean_absolute_error: 0.0049100/126 [======================>.......] - ETA: 0s - loss: 4.3116e-05 - mean_absolute_error: 0.0050122/126 [============================>.] - ETA: 0s - loss: 4.0855e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0516e-05 - mean_absolute_error: 0.0048 - val_loss: 6.7360e-05 - val_mean_absolute_error: 0.0057
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 4.8021e-05 - mean_absolute_error: 0.0054 26/126 [=====>........................] - ETA: 0s - loss: 3.6489e-05 - mean_absolute_error: 0.0044 51/126 [===========>..................] - ETA: 0s - loss: 3.4292e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.4798e-05 - mean_absolute_error: 0.0043 99/126 [======================>.......] - ETA: 0s - loss: 3.5134e-05 - mean_absolute_error: 0.0043124/126 [============================>.] - ETA: 0s - loss: 3.7647e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8009e-05 - mean_absolute_error: 0.0046 - val_loss: 1.1762e-04 - val_mean_absolute_error: 0.0089
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 4.3848e-05 - mean_absolute_error: 0.0053 26/126 [=====>........................] - ETA: 0s - loss: 4.8237e-05 - mean_absolute_error: 0.0055 50/126 [==========>...................] - ETA: 0s - loss: 4.5818e-05 - mean_absolute_error: 0.0053 70/126 [===============>..............] - ETA: 0s - loss: 4.2275e-05 - mean_absolute_error: 0.0049 94/126 [=====================>........] - ETA: 0s - loss: 4.4391e-05 - mean_absolute_error: 0.0050118/126 [===========================>..] - ETA: 0s - loss: 4.2759e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 3ms/step - loss: 4.1971e-05 - mean_absolute_error: 0.0049 - val_loss: 1.4484e-04 - val_mean_absolute_error: 0.0101
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 3.9878e-05 - mean_absolute_error: 0.0057 27/126 [=====>........................] - ETA: 0s - loss: 4.2540e-05 - mean_absolute_error: 0.0051 52/126 [===========>..................] - ETA: 0s - loss: 3.8291e-05 - mean_absolute_error: 0.0047 76/126 [=================>............] - ETA: 0s - loss: 3.7628e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.6541e-05 - mean_absolute_error: 0.0045123/126 [============================>.] - ETA: 0s - loss: 3.6928e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.6992e-05 - mean_absolute_error: 0.0045 - val_loss: 1.0008e-04 - val_mean_absolute_error: 0.0080
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 2.8289e-05 - mean_absolute_error: 0.0045 24/126 [====>.........................] - ETA: 0s - loss: 3.5147e-05 - mean_absolute_error: 0.0043 48/126 [==========>...................] - ETA: 0s - loss: 3.2756e-05 - mean_absolute_error: 0.0042 72/126 [================>.............] - ETA: 0s - loss: 3.4063e-05 - mean_absolute_error: 0.0042 97/126 [======================>.......] - ETA: 0s - loss: 3.5530e-05 - mean_absolute_error: 0.0044121/126 [===========================>..] - ETA: 0s - loss: 3.6862e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 3ms/step - loss: 3.7781e-05 - mean_absolute_error: 0.0046 - val_loss: 6.6156e-05 - val_mean_absolute_error: 0.0057
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 2.9115e-05 - mean_absolute_error: 0.0043 26/126 [=====>........................] - ETA: 0s - loss: 6.1341e-05 - mean_absolute_error: 0.0062 51/126 [===========>..................] - ETA: 0s - loss: 4.8275e-05 - mean_absolute_error: 0.0053 72/126 [================>.............] - ETA: 0s - loss: 4.2930e-05 - mean_absolute_error: 0.0049 89/126 [====================>.........] - ETA: 0s - loss: 4.1530e-05 - mean_absolute_error: 0.0049109/126 [========================>.....] - ETA: 0s - loss: 4.2375e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.1801e-05 - mean_absolute_error: 0.0048 - val_loss: 6.8339e-05 - val_mean_absolute_error: 0.0057
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 2.9467e-05 - mean_absolute_error: 0.0043 22/126 [====>.........................] - ETA: 0s - loss: 3.9178e-05 - mean_absolute_error: 0.0046 45/126 [=========>....................] - ETA: 0s - loss: 3.7087e-05 - mean_absolute_error: 0.0045 68/126 [===============>..............] - ETA: 0s - loss: 3.8430e-05 - mean_absolute_error: 0.0046 90/126 [====================>.........] - ETA: 0s - loss: 3.8414e-05 - mean_absolute_error: 0.0046112/126 [=========================>....] - ETA: 0s - loss: 4.2082e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 4.3589e-05 - mean_absolute_error: 0.0050 - val_loss: 6.7812e-05 - val_mean_absolute_error: 0.0059
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 4.8075e-05 - mean_absolute_error: 0.0046 24/126 [====>.........................] - ETA: 0s - loss: 4.3248e-05 - mean_absolute_error: 0.0049 50/126 [==========>...................] - ETA: 0s - loss: 3.8295e-05 - mean_absolute_error: 0.0045 69/126 [===============>..............] - ETA: 0s - loss: 3.8069e-05 - mean_absolute_error: 0.0045 88/126 [===================>..........] - ETA: 0s - loss: 3.9487e-05 - mean_absolute_error: 0.0046109/126 [========================>.....] - ETA: 0s - loss: 3.9223e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.8406e-05 - mean_absolute_error: 0.0046 - val_loss: 7.0308e-05 - val_mean_absolute_error: 0.0061
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 3.1511e-05 - mean_absolute_error: 0.0037 27/126 [=====>........................] - ETA: 0s - loss: 4.0772e-05 - mean_absolute_error: 0.0046 43/126 [=========>....................] - ETA: 0s - loss: 4.4815e-05 - mean_absolute_error: 0.0049 60/126 [=============>................] - ETA: 0s - loss: 4.5173e-05 - mean_absolute_error: 0.0050 81/126 [==================>...........] - ETA: 0s - loss: 4.4280e-05 - mean_absolute_error: 0.0050104/126 [=======================>......] - ETA: 0s - loss: 4.2080e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.0941e-05 - mean_absolute_error: 0.0048 - val_loss: 9.2846e-05 - val_mean_absolute_error: 0.0076
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 4.3895e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 3.4005e-05 - mean_absolute_error: 0.0043 45/126 [=========>....................] - ETA: 0s - loss: 3.4935e-05 - mean_absolute_error: 0.0043 62/126 [=============>................] - ETA: 0s - loss: 3.5946e-05 - mean_absolute_error: 0.0044 79/126 [=================>............] - ETA: 0s - loss: 4.2056e-05 - mean_absolute_error: 0.0049 96/126 [=====================>........] - ETA: 0s - loss: 4.2645e-05 - mean_absolute_error: 0.0049117/126 [==========================>...] - ETA: 0s - loss: 4.2896e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 3ms/step - loss: 4.3185e-05 - mean_absolute_error: 0.0050 - val_loss: 6.7984e-05 - val_mean_absolute_error: 0.0059
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 3.4173e-05 - mean_absolute_error: 0.0038 21/126 [====>.........................] - ETA: 0s - loss: 3.7296e-05 - mean_absolute_error: 0.0045 45/126 [=========>....................] - ETA: 0s - loss: 4.6108e-05 - mean_absolute_error: 0.0052 70/126 [===============>..............] - ETA: 0s - loss: 4.1865e-05 - mean_absolute_error: 0.0048 91/126 [====================>.........] - ETA: 0s - loss: 4.1515e-05 - mean_absolute_error: 0.0048115/126 [==========================>...] - ETA: 0s - loss: 4.1058e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 4.0482e-05 - mean_absolute_error: 0.0047 - val_loss: 6.5529e-05 - val_mean_absolute_error: 0.0057
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 7.0143e-05 - mean_absolute_error: 0.0044 27/126 [=====>........................] - ETA: 0s - loss: 4.0411e-05 - mean_absolute_error: 0.0046 51/126 [===========>..................] - ETA: 0s - loss: 3.7371e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 3.8023e-05 - mean_absolute_error: 0.0046101/126 [=======================>......] - ETA: 0s - loss: 3.6775e-05 - mean_absolute_error: 0.0045120/126 [===========================>..] - ETA: 0s - loss: 3.6498e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.6664e-05 - mean_absolute_error: 0.0045 - val_loss: 9.4943e-05 - val_mean_absolute_error: 0.0077
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 2.6163e-05 - mean_absolute_error: 0.0041 26/126 [=====>........................] - ETA: 0s - loss: 3.8875e-05 - mean_absolute_error: 0.0045 51/126 [===========>..................] - ETA: 0s - loss: 3.8902e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 4.2970e-05 - mean_absolute_error: 0.0050100/126 [======================>.......] - ETA: 0s - loss: 4.0947e-05 - mean_absolute_error: 0.0048124/126 [============================>.] - ETA: 0s - loss: 3.9939e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9802e-05 - mean_absolute_error: 0.0047 - val_loss: 7.8710e-05 - val_mean_absolute_error: 0.0068
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 3.4512e-05 - mean_absolute_error: 0.0036 22/126 [====>.........................] - ETA: 0s - loss: 3.9886e-05 - mean_absolute_error: 0.0048 44/126 [=========>....................] - ETA: 0s - loss: 3.8868e-05 - mean_absolute_error: 0.0047 66/126 [==============>...............] - ETA: 0s - loss: 3.8307e-05 - mean_absolute_error: 0.0046 83/126 [==================>...........] - ETA: 0s - loss: 3.9720e-05 - mean_absolute_error: 0.0048101/126 [=======================>......] - ETA: 0s - loss: 3.9973e-05 - mean_absolute_error: 0.0048121/126 [===========================>..] - ETA: 0s - loss: 3.9676e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 4.0639e-05 - mean_absolute_error: 0.0048 - val_loss: 8.2803e-05 - val_mean_absolute_error: 0.0067
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 6.5351e-05 - mean_absolute_error: 0.0069 23/126 [====>.........................] - ETA: 0s - loss: 4.0286e-05 - mean_absolute_error: 0.0048 46/126 [=========>....................] - ETA: 0s - loss: 3.8098e-05 - mean_absolute_error: 0.0047 69/126 [===============>..............] - ETA: 0s - loss: 3.6718e-05 - mean_absolute_error: 0.0044 86/126 [===================>..........] - ETA: 0s - loss: 3.5721e-05 - mean_absolute_error: 0.0044101/126 [=======================>......] - ETA: 0s - loss: 3.7330e-05 - mean_absolute_error: 0.0045126/126 [==============================] - ETA: 0s - loss: 3.5984e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5984e-05 - mean_absolute_error: 0.0044 - val_loss: 7.0699e-05 - val_mean_absolute_error: 0.0062
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 2.7451e-05 - mean_absolute_error: 0.0037 22/126 [====>.........................] - ETA: 0s - loss: 3.8733e-05 - mean_absolute_error: 0.0045 47/126 [==========>...................] - ETA: 0s - loss: 3.8993e-05 - mean_absolute_error: 0.0045 71/126 [===============>..............] - ETA: 0s - loss: 3.8531e-05 - mean_absolute_error: 0.0046 96/126 [=====================>........] - ETA: 0s - loss: 3.8436e-05 - mean_absolute_error: 0.0046119/126 [===========================>..] - ETA: 0s - loss: 4.0379e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9959e-05 - mean_absolute_error: 0.0048 - val_loss: 6.7634e-05 - val_mean_absolute_error: 0.0060
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 1.7130e-05 - mean_absolute_error: 0.0025 25/126 [====>.........................] - ETA: 0s - loss: 3.7105e-05 - mean_absolute_error: 0.0045 49/126 [==========>...................] - ETA: 0s - loss: 3.8317e-05 - mean_absolute_error: 0.0045 74/126 [================>.............] - ETA: 0s - loss: 3.7486e-05 - mean_absolute_error: 0.0046 97/126 [======================>.......] - ETA: 0s - loss: 3.9660e-05 - mean_absolute_error: 0.0048120/126 [===========================>..] - ETA: 0s - loss: 3.9454e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 3.9690e-05 - mean_absolute_error: 0.0048 - val_loss: 1.1533e-04 - val_mean_absolute_error: 0.0088
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 3.7078e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 5.0531e-05 - mean_absolute_error: 0.0056 49/126 [==========>...................] - ETA: 0s - loss: 4.4055e-05 - mean_absolute_error: 0.0051 73/126 [================>.............] - ETA: 0s - loss: 4.7112e-05 - mean_absolute_error: 0.0053 97/126 [======================>.......] - ETA: 0s - loss: 4.5912e-05 - mean_absolute_error: 0.0052121/126 [===========================>..] - ETA: 0s - loss: 4.8071e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 2ms/step - loss: 4.9228e-05 - mean_absolute_error: 0.0054 - val_loss: 1.9084e-04 - val_mean_absolute_error: 0.0117
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 1.6821e-04 - mean_absolute_error: 0.0120 27/126 [=====>........................] - ETA: 0s - loss: 5.1031e-05 - mean_absolute_error: 0.0054 51/126 [===========>..................] - ETA: 0s - loss: 4.2167e-05 - mean_absolute_error: 0.0048 70/126 [===============>..............] - ETA: 0s - loss: 4.5408e-05 - mean_absolute_error: 0.0050 88/126 [===================>..........] - ETA: 0s - loss: 4.3487e-05 - mean_absolute_error: 0.0049107/126 [========================>.....] - ETA: 0s - loss: 4.2925e-05 - mean_absolute_error: 0.0049124/126 [============================>.] - ETA: 0s - loss: 4.2826e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.3209e-05 - mean_absolute_error: 0.0049 - val_loss: 1.3138e-04 - val_mean_absolute_error: 0.0096
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 6.9029e-05 - mean_absolute_error: 0.0068 13/126 [==>...........................] - ETA: 0s - loss: 3.8777e-05 - mean_absolute_error: 0.0048 26/126 [=====>........................] - ETA: 0s - loss: 5.2835e-05 - mean_absolute_error: 0.0057 42/126 [=========>....................] - ETA: 0s - loss: 5.0153e-05 - mean_absolute_error: 0.0056 57/126 [============>.................] - ETA: 0s - loss: 4.6260e-05 - mean_absolute_error: 0.0052 74/126 [================>.............] - ETA: 0s - loss: 4.4690e-05 - mean_absolute_error: 0.0051 95/126 [=====================>........] - ETA: 0s - loss: 4.2859e-05 - mean_absolute_error: 0.0049113/126 [=========================>....] - ETA: 0s - loss: 4.1777e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 4ms/step - loss: 4.1173e-05 - mean_absolute_error: 0.0048 - val_loss: 6.9979e-05 - val_mean_absolute_error: 0.0061
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 2.6237e-05 - mean_absolute_error: 0.0033 17/126 [===>..........................] - ETA: 0s - loss: 3.1177e-05 - mean_absolute_error: 0.0042 33/126 [======>.......................] - ETA: 0s - loss: 3.3649e-05 - mean_absolute_error: 0.0044 47/126 [==========>...................] - ETA: 0s - loss: 3.5730e-05 - mean_absolute_error: 0.0045 61/126 [=============>................] - ETA: 0s - loss: 3.6614e-05 - mean_absolute_error: 0.0046 73/126 [================>.............] - ETA: 0s - loss: 3.8656e-05 - mean_absolute_error: 0.0047 87/126 [===================>..........] - ETA: 0s - loss: 3.9877e-05 - mean_absolute_error: 0.0048100/126 [======================>.......] - ETA: 0s - loss: 3.9501e-05 - mean_absolute_error: 0.0047114/126 [==========================>...] - ETA: 0s - loss: 4.0399e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 1s 4ms/step - loss: 4.2552e-05 - mean_absolute_error: 0.0050 - val_loss: 1.3559e-04 - val_mean_absolute_error: 0.0094
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 7.8531e-05 - mean_absolute_error: 0.0079 19/126 [===>..........................] - ETA: 0s - loss: 4.7009e-05 - mean_absolute_error: 0.0053 37/126 [=======>......................] - ETA: 0s - loss: 4.0432e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 3.9777e-05 - mean_absolute_error: 0.0047 64/126 [==============>...............] - ETA: 0s - loss: 3.9451e-05 - mean_absolute_error: 0.0047 78/126 [=================>............] - ETA: 0s - loss: 3.9299e-05 - mean_absolute_error: 0.0047 91/126 [====================>.........] - ETA: 0s - loss: 3.8752e-05 - mean_absolute_error: 0.0046104/126 [=======================>......] - ETA: 0s - loss: 3.9446e-05 - mean_absolute_error: 0.0047117/126 [==========================>...] - ETA: 0s - loss: 4.0203e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 1s 4ms/step - loss: 4.0246e-05 - mean_absolute_error: 0.0048 - val_loss: 1.3857e-04 - val_mean_absolute_error: 0.0099
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 6.4256e-05 - mean_absolute_error: 0.0071 16/126 [==>...........................] - ETA: 0s - loss: 3.5275e-05 - mean_absolute_error: 0.0046 30/126 [======>.......................] - ETA: 0s - loss: 3.6047e-05 - mean_absolute_error: 0.0044 42/126 [=========>....................] - ETA: 0s - loss: 3.8075e-05 - mean_absolute_error: 0.0046 55/126 [============>.................] - ETA: 0s - loss: 3.9760e-05 - mean_absolute_error: 0.0048 70/126 [===============>..............] - ETA: 0s - loss: 4.0504e-05 - mean_absolute_error: 0.0048 84/126 [===================>..........] - ETA: 0s - loss: 4.1171e-05 - mean_absolute_error: 0.0048 96/126 [=====================>........] - ETA: 0s - loss: 4.1534e-05 - mean_absolute_error: 0.0049112/126 [=========================>....] - ETA: 0s - loss: 4.0428e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 1s 4ms/step - loss: 4.0422e-05 - mean_absolute_error: 0.0048 - val_loss: 1.4863e-04 - val_mean_absolute_error: 0.0103
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 7.2101e-05 - mean_absolute_error: 0.0077 14/126 [==>...........................] - ETA: 0s - loss: 4.1211e-05 - mean_absolute_error: 0.0050 27/126 [=====>........................] - ETA: 0s - loss: 4.3871e-05 - mean_absolute_error: 0.0052 43/126 [=========>....................] - ETA: 0s - loss: 4.3044e-05 - mean_absolute_error: 0.0050 59/126 [=============>................] - ETA: 0s - loss: 4.2769e-05 - mean_absolute_error: 0.0050 75/126 [================>.............] - ETA: 0s - loss: 4.0981e-05 - mean_absolute_error: 0.0049 89/126 [====================>.........] - ETA: 0s - loss: 3.9864e-05 - mean_absolute_error: 0.0048107/126 [========================>.....] - ETA: 0s - loss: 3.9938e-05 - mean_absolute_error: 0.0048125/126 [============================>.] - ETA: 0s - loss: 3.9316e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 4ms/step - loss: 3.9326e-05 - mean_absolute_error: 0.0047 - val_loss: 8.3260e-05 - val_mean_absolute_error: 0.0071
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 2.7124e-05 - mean_absolute_error: 0.0039 15/126 [==>...........................] - ETA: 0s - loss: 3.5349e-05 - mean_absolute_error: 0.0044 29/126 [=====>........................] - ETA: 0s - loss: 3.7935e-05 - mean_absolute_error: 0.0046 44/126 [=========>....................] - ETA: 0s - loss: 4.0671e-05 - mean_absolute_error: 0.0047 61/126 [=============>................] - ETA: 0s - loss: 4.1614e-05 - mean_absolute_error: 0.0048 81/126 [==================>...........] - ETA: 0s - loss: 4.0329e-05 - mean_absolute_error: 0.0047 98/126 [======================>.......] - ETA: 0s - loss: 3.9790e-05 - mean_absolute_error: 0.0048115/126 [==========================>...] - ETA: 0s - loss: 3.9421e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 3ms/step - loss: 3.8803e-05 - mean_absolute_error: 0.0047 - val_loss: 6.8385e-05 - val_mean_absolute_error: 0.0061
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 2.8331e-05 - mean_absolute_error: 0.0039 21/126 [====>.........................] - ETA: 0s - loss: 3.3352e-05 - mean_absolute_error: 0.0043 42/126 [=========>....................] - ETA: 0s - loss: 3.3856e-05 - mean_absolute_error: 0.0043 65/126 [==============>...............] - ETA: 0s - loss: 3.6877e-05 - mean_absolute_error: 0.0045 87/126 [===================>..........] - ETA: 0s - loss: 3.6463e-05 - mean_absolute_error: 0.0045108/126 [========================>.....] - ETA: 0s - loss: 3.9409e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 3ms/step - loss: 4.0469e-05 - mean_absolute_error: 0.0048 - val_loss: 1.1394e-04 - val_mean_absolute_error: 0.0084
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 8.1296e-05 - mean_absolute_error: 0.0077 25/126 [====>.........................] - ETA: 0s - loss: 4.8265e-05 - mean_absolute_error: 0.0055 43/126 [=========>....................] - ETA: 0s - loss: 4.4026e-05 - mean_absolute_error: 0.0051 66/126 [==============>...............] - ETA: 0s - loss: 4.3509e-05 - mean_absolute_error: 0.0050 87/126 [===================>..........] - ETA: 0s - loss: 4.2398e-05 - mean_absolute_error: 0.0049110/126 [=========================>....] - ETA: 0s - loss: 4.1841e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.1367e-05 - mean_absolute_error: 0.0048 - val_loss: 6.8112e-05 - val_mean_absolute_error: 0.0058
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 3.5726e-05 - mean_absolute_error: 0.0051 19/126 [===>..........................] - ETA: 0s - loss: 3.2352e-05 - mean_absolute_error: 0.0043 39/126 [========>.....................] - ETA: 0s - loss: 3.3573e-05 - mean_absolute_error: 0.0044 64/126 [==============>...............] - ETA: 0s - loss: 4.0226e-05 - mean_absolute_error: 0.0048 81/126 [==================>...........] - ETA: 0s - loss: 4.0372e-05 - mean_absolute_error: 0.0049104/126 [=======================>......] - ETA: 0s - loss: 4.1044e-05 - mean_absolute_error: 0.0049125/126 [============================>.] - ETA: 0s - loss: 4.1265e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.1235e-05 - mean_absolute_error: 0.0049 - val_loss: 7.2112e-05 - val_mean_absolute_error: 0.0064
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 2.4751e-05 - mean_absolute_error: 0.0036 20/126 [===>..........................] - ETA: 0s - loss: 3.5329e-05 - mean_absolute_error: 0.0045 40/126 [========>.....................] - ETA: 0s - loss: 3.7958e-05 - mean_absolute_error: 0.0047 62/126 [=============>................] - ETA: 0s - loss: 3.7462e-05 - mean_absolute_error: 0.0047 84/126 [===================>..........] - ETA: 0s - loss: 4.1948e-05 - mean_absolute_error: 0.0049106/126 [========================>.....] - ETA: 0s - loss: 4.6526e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 3ms/step - loss: 4.8294e-05 - mean_absolute_error: 0.0054 - val_loss: 6.2104e-05 - val_mean_absolute_error: 0.0055
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 4.4834e-05 - mean_absolute_error: 0.0049 25/126 [====>.........................] - ETA: 0s - loss: 4.6239e-05 - mean_absolute_error: 0.0054 49/126 [==========>...................] - ETA: 0s - loss: 3.9042e-05 - mean_absolute_error: 0.0048 68/126 [===============>..............] - ETA: 0s - loss: 4.0488e-05 - mean_absolute_error: 0.0048 86/126 [===================>..........] - ETA: 0s - loss: 3.9766e-05 - mean_absolute_error: 0.0048102/126 [=======================>......] - ETA: 0s - loss: 3.8835e-05 - mean_absolute_error: 0.0047123/126 [============================>.] - ETA: 0s - loss: 3.8967e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 3ms/step - loss: 3.8903e-05 - mean_absolute_error: 0.0047 - val_loss: 6.4268e-05 - val_mean_absolute_error: 0.0057
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 2.3860e-05 - mean_absolute_error: 0.0033 24/126 [====>.........................] - ETA: 0s - loss: 3.6610e-05 - mean_absolute_error: 0.0046 48/126 [==========>...................] - ETA: 0s - loss: 3.5592e-05 - mean_absolute_error: 0.0045 70/126 [===============>..............] - ETA: 0s - loss: 3.3579e-05 - mean_absolute_error: 0.0043 89/126 [====================>.........] - ETA: 0s - loss: 3.3922e-05 - mean_absolute_error: 0.0043112/126 [=========================>....] - ETA: 0s - loss: 3.5485e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5627e-05 - mean_absolute_error: 0.0044 - val_loss: 8.3348e-05 - val_mean_absolute_error: 0.0071
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 2.3529e-05 - mean_absolute_error: 0.0036 21/126 [====>.........................] - ETA: 0s - loss: 3.5971e-05 - mean_absolute_error: 0.0046 35/126 [=======>......................] - ETA: 0s - loss: 3.7483e-05 - mean_absolute_error: 0.0047 49/126 [==========>...................] - ETA: 0s - loss: 4.3229e-05 - mean_absolute_error: 0.0051 66/126 [==============>...............] - ETA: 0s - loss: 4.2228e-05 - mean_absolute_error: 0.0050 85/126 [===================>..........] - ETA: 0s - loss: 4.2665e-05 - mean_absolute_error: 0.0050103/126 [=======================>......] - ETA: 0s - loss: 4.3985e-05 - mean_absolute_error: 0.0050124/126 [============================>.] - ETA: 0s - loss: 4.3193e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 3ms/step - loss: 4.3062e-05 - mean_absolute_error: 0.0050 - val_loss: 8.1293e-05 - val_mean_absolute_error: 0.0070
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 3.6887e-05 - mean_absolute_error: 0.0048 16/126 [==>...........................] - ETA: 0s - loss: 4.2931e-05 - mean_absolute_error: 0.0051 35/126 [=======>......................] - ETA: 0s - loss: 3.7934e-05 - mean_absolute_error: 0.0048 58/126 [============>.................] - ETA: 0s - loss: 4.2027e-05 - mean_absolute_error: 0.0050 80/126 [==================>...........] - ETA: 0s - loss: 4.1630e-05 - mean_absolute_error: 0.0050103/126 [=======================>......] - ETA: 0s - loss: 4.1571e-05 - mean_absolute_error: 0.0050125/126 [============================>.] - ETA: 0s - loss: 4.1672e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.1596e-05 - mean_absolute_error: 0.0049 - val_loss: 6.2501e-05 - val_mean_absolute_error: 0.0056
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 3.5962e-05 - mean_absolute_error: 0.0045 24/126 [====>.........................] - ETA: 0s - loss: 3.6218e-05 - mean_absolute_error: 0.0046 48/126 [==========>...................] - ETA: 0s - loss: 3.6497e-05 - mean_absolute_error: 0.0046 71/126 [===============>..............] - ETA: 0s - loss: 3.6091e-05 - mean_absolute_error: 0.0045 95/126 [=====================>........] - ETA: 0s - loss: 4.5933e-05 - mean_absolute_error: 0.0051119/126 [===========================>..] - ETA: 0s - loss: 4.5803e-05 - mean_absolute_error: 0.0051126/126 [==============================] - 0s 3ms/step - loss: 4.5960e-05 - mean_absolute_error: 0.0051 - val_loss: 6.4205e-05 - val_mean_absolute_error: 0.0058
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 5.6676e-05 - mean_absolute_error: 0.0047 24/126 [====>.........................] - ETA: 0s - loss: 4.8878e-05 - mean_absolute_error: 0.0056 43/126 [=========>....................] - ETA: 0s - loss: 5.6073e-05 - mean_absolute_error: 0.0060 62/126 [=============>................] - ETA: 0s - loss: 5.5611e-05 - mean_absolute_error: 0.0059 79/126 [=================>............] - ETA: 0s - loss: 5.0546e-05 - mean_absolute_error: 0.0055102/126 [=======================>......] - ETA: 0s - loss: 4.6942e-05 - mean_absolute_error: 0.0052124/126 [============================>.] - ETA: 0s - loss: 4.4446e-05 - mean_absolute_error: 0.0051126/126 [==============================] - 0s 3ms/step - loss: 4.4583e-05 - mean_absolute_error: 0.0051 - val_loss: 6.2081e-05 - val_mean_absolute_error: 0.0055
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 2.9717e-05 - mean_absolute_error: 0.0041 25/126 [====>.........................] - ETA: 0s - loss: 3.4519e-05 - mean_absolute_error: 0.0044 48/126 [==========>...................] - ETA: 0s - loss: 3.6896e-05 - mean_absolute_error: 0.0046 70/126 [===============>..............] - ETA: 0s - loss: 4.0087e-05 - mean_absolute_error: 0.0047 94/126 [=====================>........] - ETA: 0s - loss: 3.9059e-05 - mean_absolute_error: 0.0047119/126 [===========================>..] - ETA: 0s - loss: 3.8410e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8073e-05 - mean_absolute_error: 0.0046 - val_loss: 7.6740e-05 - val_mean_absolute_error: 0.0064
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 3.5349e-05 - mean_absolute_error: 0.0051 23/126 [====>.........................] - ETA: 0s - loss: 3.3885e-05 - mean_absolute_error: 0.0044 44/126 [=========>....................] - ETA: 0s - loss: 3.2983e-05 - mean_absolute_error: 0.0042 68/126 [===============>..............] - ETA: 0s - loss: 4.0310e-05 - mean_absolute_error: 0.0047 90/126 [====================>.........] - ETA: 0s - loss: 4.7195e-05 - mean_absolute_error: 0.0052112/126 [=========================>....] - ETA: 0s - loss: 4.9055e-05 - mean_absolute_error: 0.0054126/126 [==============================] - 0s 3ms/step - loss: 4.8923e-05 - mean_absolute_error: 0.0054 - val_loss: 7.3944e-05 - val_mean_absolute_error: 0.0063
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 5.9060e-05 - mean_absolute_error: 0.0060 23/126 [====>.........................] - ETA: 0s - loss: 3.9398e-05 - mean_absolute_error: 0.0047 48/126 [==========>...................] - ETA: 0s - loss: 3.9055e-05 - mean_absolute_error: 0.0048 73/126 [================>.............] - ETA: 0s - loss: 3.9100e-05 - mean_absolute_error: 0.0048 95/126 [=====================>........] - ETA: 0s - loss: 3.8340e-05 - mean_absolute_error: 0.0046119/126 [===========================>..] - ETA: 0s - loss: 3.8833e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9089e-05 - mean_absolute_error: 0.0047 - val_loss: 6.1113e-05 - val_mean_absolute_error: 0.0054
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 2.2282e-05 - mean_absolute_error: 0.0036 27/126 [=====>........................] - ETA: 0s - loss: 4.2087e-05 - mean_absolute_error: 0.0050 52/126 [===========>..................] - ETA: 0s - loss: 3.6563e-05 - mean_absolute_error: 0.0045 75/126 [================>.............] - ETA: 0s - loss: 3.6995e-05 - mean_absolute_error: 0.0046 99/126 [======================>.......] - ETA: 0s - loss: 3.7146e-05 - mean_absolute_error: 0.0046123/126 [============================>.] - ETA: 0s - loss: 3.9992e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0065e-05 - mean_absolute_error: 0.0048 - val_loss: 6.8125e-05 - val_mean_absolute_error: 0.0059
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 7.4191e-05 - mean_absolute_error: 0.0053 26/126 [=====>........................] - ETA: 0s - loss: 3.5313e-05 - mean_absolute_error: 0.0045 48/126 [==========>...................] - ETA: 0s - loss: 3.6686e-05 - mean_absolute_error: 0.0046 70/126 [===============>..............] - ETA: 0s - loss: 3.7169e-05 - mean_absolute_error: 0.0046 94/126 [=====================>........] - ETA: 0s - loss: 3.7256e-05 - mean_absolute_error: 0.0045117/126 [==========================>...] - ETA: 0s - loss: 3.5781e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5743e-05 - mean_absolute_error: 0.0044 - val_loss: 6.1023e-05 - val_mean_absolute_error: 0.0054
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 6.6283e-05 - mean_absolute_error: 0.0064 21/126 [====>.........................] - ETA: 0s - loss: 3.6518e-05 - mean_absolute_error: 0.0044 40/126 [========>.....................] - ETA: 0s - loss: 3.4800e-05 - mean_absolute_error: 0.0044 60/126 [=============>................] - ETA: 0s - loss: 3.8633e-05 - mean_absolute_error: 0.0047 78/126 [=================>............] - ETA: 0s - loss: 3.9223e-05 - mean_absolute_error: 0.0048 97/126 [======================>.......] - ETA: 0s - loss: 4.0543e-05 - mean_absolute_error: 0.0049118/126 [===========================>..] - ETA: 0s - loss: 3.9376e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9399e-05 - mean_absolute_error: 0.0048 - val_loss: 7.3233e-05 - val_mean_absolute_error: 0.0065
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 3.1383e-05 - mean_absolute_error: 0.0046 21/126 [====>.........................] - ETA: 0s - loss: 2.9335e-05 - mean_absolute_error: 0.0040 38/126 [========>.....................] - ETA: 0s - loss: 3.9398e-05 - mean_absolute_error: 0.0047 57/126 [============>.................] - ETA: 0s - loss: 4.0527e-05 - mean_absolute_error: 0.0048 79/126 [=================>............] - ETA: 0s - loss: 4.0628e-05 - mean_absolute_error: 0.0047104/126 [=======================>......] - ETA: 0s - loss: 3.8261e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.6672e-05 - mean_absolute_error: 0.0045 - val_loss: 1.2077e-04 - val_mean_absolute_error: 0.0091
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 3.8886e-05 - mean_absolute_error: 0.0050 26/126 [=====>........................] - ETA: 0s - loss: 3.5554e-05 - mean_absolute_error: 0.0045 48/126 [==========>...................] - ETA: 0s - loss: 3.8126e-05 - mean_absolute_error: 0.0047 70/126 [===============>..............] - ETA: 0s - loss: 3.5702e-05 - mean_absolute_error: 0.0045 91/126 [====================>.........] - ETA: 0s - loss: 3.4742e-05 - mean_absolute_error: 0.0044116/126 [==========================>...] - ETA: 0s - loss: 3.6784e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.6526e-05 - mean_absolute_error: 0.0045 - val_loss: 9.7314e-05 - val_mean_absolute_error: 0.0080
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 5.8735e-05 - mean_absolute_error: 0.0059 26/126 [=====>........................] - ETA: 0s - loss: 3.3690e-05 - mean_absolute_error: 0.0044 52/126 [===========>..................] - ETA: 0s - loss: 3.6561e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 4.0426e-05 - mean_absolute_error: 0.0048102/126 [=======================>......] - ETA: 0s - loss: 3.9333e-05 - mean_absolute_error: 0.0047126/126 [==============================] - ETA: 0s - loss: 3.7883e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7883e-05 - mean_absolute_error: 0.0046 - val_loss: 7.7599e-05 - val_mean_absolute_error: 0.0068
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 3.4597e-05 - mean_absolute_error: 0.0047 27/126 [=====>........................] - ETA: 0s - loss: 3.9166e-05 - mean_absolute_error: 0.0046 53/126 [===========>..................] - ETA: 0s - loss: 3.8929e-05 - mean_absolute_error: 0.0046 78/126 [=================>............] - ETA: 0s - loss: 3.9291e-05 - mean_absolute_error: 0.0048 98/126 [======================>.......] - ETA: 0s - loss: 4.6505e-05 - mean_absolute_error: 0.0052124/126 [============================>.] - ETA: 0s - loss: 4.6875e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 2ms/step - loss: 4.6741e-05 - mean_absolute_error: 0.0053 - val_loss: 5.9945e-05 - val_mean_absolute_error: 0.0054
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 2.5277e-05 - mean_absolute_error: 0.0035 24/126 [====>.........................] - ETA: 0s - loss: 2.8802e-05 - mean_absolute_error: 0.0039 48/126 [==========>...................] - ETA: 0s - loss: 3.5096e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.6773e-05 - mean_absolute_error: 0.0045 99/126 [======================>.......] - ETA: 0s - loss: 3.7169e-05 - mean_absolute_error: 0.0045118/126 [===========================>..] - ETA: 0s - loss: 3.6504e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.6765e-05 - mean_absolute_error: 0.0045 - val_loss: 9.0929e-05 - val_mean_absolute_error: 0.0073
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 4.5518e-05 - mean_absolute_error: 0.0057 25/126 [====>.........................] - ETA: 0s - loss: 5.3974e-05 - mean_absolute_error: 0.0057 48/126 [==========>...................] - ETA: 0s - loss: 4.4996e-05 - mean_absolute_error: 0.0052 71/126 [===============>..............] - ETA: 0s - loss: 4.3395e-05 - mean_absolute_error: 0.0050 94/126 [=====================>........] - ETA: 0s - loss: 4.1946e-05 - mean_absolute_error: 0.0049120/126 [===========================>..] - ETA: 0s - loss: 4.0931e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.0444e-05 - mean_absolute_error: 0.0049 - val_loss: 6.1044e-05 - val_mean_absolute_error: 0.0054
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 3.3453e-05 - mean_absolute_error: 0.0047 27/126 [=====>........................] - ETA: 0s - loss: 3.3656e-05 - mean_absolute_error: 0.0042 47/126 [==========>...................] - ETA: 0s - loss: 3.2852e-05 - mean_absolute_error: 0.0042 66/126 [==============>...............] - ETA: 0s - loss: 3.7626e-05 - mean_absolute_error: 0.0045 85/126 [===================>..........] - ETA: 0s - loss: 3.6833e-05 - mean_absolute_error: 0.0045103/126 [=======================>......] - ETA: 0s - loss: 3.6538e-05 - mean_absolute_error: 0.0045116/126 [==========================>...] - ETA: 0s - loss: 3.6560e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 3ms/step - loss: 3.7340e-05 - mean_absolute_error: 0.0046 - val_loss: 5.9526e-05 - val_mean_absolute_error: 0.0053
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 3.2018e-05 - mean_absolute_error: 0.0043 23/126 [====>.........................] - ETA: 0s - loss: 3.3915e-05 - mean_absolute_error: 0.0044 40/126 [========>.....................] - ETA: 0s - loss: 3.3069e-05 - mean_absolute_error: 0.0044 61/126 [=============>................] - ETA: 0s - loss: 3.5419e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 3.8136e-05 - mean_absolute_error: 0.0047 90/126 [====================>.........] - ETA: 0s - loss: 3.9365e-05 - mean_absolute_error: 0.0049105/126 [========================>.....] - ETA: 0s - loss: 3.9710e-05 - mean_absolute_error: 0.0049123/126 [============================>.] - ETA: 0s - loss: 4.0890e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 4ms/step - loss: 4.1447e-05 - mean_absolute_error: 0.0049 - val_loss: 8.7370e-05 - val_mean_absolute_error: 0.0074
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 3.6087e-05 - mean_absolute_error: 0.0047 23/126 [====>.........................] - ETA: 0s - loss: 3.8380e-05 - mean_absolute_error: 0.0047 45/126 [=========>....................] - ETA: 0s - loss: 4.0351e-05 - mean_absolute_error: 0.0049 67/126 [==============>...............] - ETA: 0s - loss: 3.9592e-05 - mean_absolute_error: 0.0049 89/126 [====================>.........] - ETA: 0s - loss: 4.1831e-05 - mean_absolute_error: 0.0050113/126 [=========================>....] - ETA: 0s - loss: 3.9574e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9511e-05 - mean_absolute_error: 0.0048 - val_loss: 7.3344e-05 - val_mean_absolute_error: 0.0063
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 3.3588e-05 - mean_absolute_error: 0.0047 17/126 [===>..........................] - ETA: 0s - loss: 3.2288e-05 - mean_absolute_error: 0.0043 34/126 [=======>......................] - ETA: 0s - loss: 3.3099e-05 - mean_absolute_error: 0.0043 59/126 [=============>................] - ETA: 0s - loss: 3.1557e-05 - mean_absolute_error: 0.0042 83/126 [==================>...........] - ETA: 0s - loss: 3.1585e-05 - mean_absolute_error: 0.0041109/126 [========================>.....] - ETA: 0s - loss: 3.2902e-05 - mean_absolute_error: 0.0041126/126 [==============================] - 0s 3ms/step - loss: 3.4810e-05 - mean_absolute_error: 0.0043 - val_loss: 6.0194e-05 - val_mean_absolute_error: 0.0055
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 2.0369e-05 - mean_absolute_error: 0.0031 19/126 [===>..........................] - ETA: 0s - loss: 3.1582e-05 - mean_absolute_error: 0.0040 36/126 [=======>......................] - ETA: 0s - loss: 3.0301e-05 - mean_absolute_error: 0.0039 53/126 [===========>..................] - ETA: 0s - loss: 3.3182e-05 - mean_absolute_error: 0.0043 74/126 [================>.............] - ETA: 0s - loss: 3.3090e-05 - mean_absolute_error: 0.0043 96/126 [=====================>........] - ETA: 0s - loss: 3.2887e-05 - mean_absolute_error: 0.0043114/126 [==========================>...] - ETA: 0s - loss: 3.3027e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.6621e-05 - mean_absolute_error: 0.0045 - val_loss: 6.7905e-05 - val_mean_absolute_error: 0.0059
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 5.4586e-05 - mean_absolute_error: 0.0059 27/126 [=====>........................] - ETA: 0s - loss: 3.3636e-05 - mean_absolute_error: 0.0042 49/126 [==========>...................] - ETA: 0s - loss: 3.2645e-05 - mean_absolute_error: 0.0042 64/126 [==============>...............] - ETA: 0s - loss: 3.3920e-05 - mean_absolute_error: 0.0042 87/126 [===================>..........] - ETA: 0s - loss: 3.6155e-05 - mean_absolute_error: 0.0044110/126 [=========================>....] - ETA: 0s - loss: 3.5376e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.4651e-05 - mean_absolute_error: 0.0043 - val_loss: 5.8826e-05 - val_mean_absolute_error: 0.0052
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 3.4180e-05 - mean_absolute_error: 0.0042 23/126 [====>.........................] - ETA: 0s - loss: 3.1459e-05 - mean_absolute_error: 0.0041 47/126 [==========>...................] - ETA: 0s - loss: 3.5364e-05 - mean_absolute_error: 0.0044 70/126 [===============>..............] - ETA: 0s - loss: 3.7248e-05 - mean_absolute_error: 0.0046 93/126 [=====================>........] - ETA: 0s - loss: 3.6124e-05 - mean_absolute_error: 0.0044112/126 [=========================>....] - ETA: 0s - loss: 3.5396e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5401e-05 - mean_absolute_error: 0.0044 - val_loss: 1.3977e-04 - val_mean_absolute_error: 0.0101
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 8.3030e-05 - mean_absolute_error: 0.0081 27/126 [=====>........................] - ETA: 0s - loss: 5.7673e-05 - mean_absolute_error: 0.0060 48/126 [==========>...................] - ETA: 0s - loss: 4.8067e-05 - mean_absolute_error: 0.0054 69/126 [===============>..............] - ETA: 0s - loss: 4.5438e-05 - mean_absolute_error: 0.0052 92/126 [====================>.........] - ETA: 0s - loss: 4.1292e-05 - mean_absolute_error: 0.0049112/126 [=========================>....] - ETA: 0s - loss: 3.9601e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9836e-05 - mean_absolute_error: 0.0048 - val_loss: 5.7208e-05 - val_mean_absolute_error: 0.0052
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 1.9347e-05 - mean_absolute_error: 0.0029 21/126 [====>.........................] - ETA: 0s - loss: 3.6971e-05 - mean_absolute_error: 0.0045 40/126 [========>.....................] - ETA: 0s - loss: 3.7850e-05 - mean_absolute_error: 0.0046 59/126 [=============>................] - ETA: 0s - loss: 3.6467e-05 - mean_absolute_error: 0.0045 82/126 [==================>...........] - ETA: 0s - loss: 3.4870e-05 - mean_absolute_error: 0.0043105/126 [========================>.....] - ETA: 0s - loss: 3.5205e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.6997e-05 - mean_absolute_error: 0.0046 - val_loss: 7.5975e-05 - val_mean_absolute_error: 0.0068
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 2.7140e-05 - mean_absolute_error: 0.0044 25/126 [====>.........................] - ETA: 0s - loss: 3.4093e-05 - mean_absolute_error: 0.0044 48/126 [==========>...................] - ETA: 0s - loss: 3.8563e-05 - mean_absolute_error: 0.0047 66/126 [==============>...............] - ETA: 0s - loss: 3.7053e-05 - mean_absolute_error: 0.0046 83/126 [==================>...........] - ETA: 0s - loss: 3.4869e-05 - mean_absolute_error: 0.0044105/126 [========================>.....] - ETA: 0s - loss: 3.4378e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.4585e-05 - mean_absolute_error: 0.0043 - val_loss: 6.0732e-05 - val_mean_absolute_error: 0.0056
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 2.5821e-05 - mean_absolute_error: 0.0035 20/126 [===>..........................] - ETA: 0s - loss: 3.3355e-05 - mean_absolute_error: 0.0041 40/126 [========>.....................] - ETA: 0s - loss: 3.7025e-05 - mean_absolute_error: 0.0045 59/126 [=============>................] - ETA: 0s - loss: 3.5328e-05 - mean_absolute_error: 0.0044 71/126 [===============>..............] - ETA: 0s - loss: 3.6382e-05 - mean_absolute_error: 0.0045 85/126 [===================>..........] - ETA: 0s - loss: 3.7564e-05 - mean_absolute_error: 0.0046100/126 [======================>.......] - ETA: 0s - loss: 3.7999e-05 - mean_absolute_error: 0.0046115/126 [==========================>...] - ETA: 0s - loss: 3.8183e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 4ms/step - loss: 3.7805e-05 - mean_absolute_error: 0.0047 - val_loss: 5.8019e-05 - val_mean_absolute_error: 0.0054
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 2.3435e-05 - mean_absolute_error: 0.0033 15/126 [==>...........................] - ETA: 0s - loss: 3.2044e-05 - mean_absolute_error: 0.0039 29/126 [=====>........................] - ETA: 0s - loss: 3.3363e-05 - mean_absolute_error: 0.0041 45/126 [=========>....................] - ETA: 0s - loss: 3.2852e-05 - mean_absolute_error: 0.0042 64/126 [==============>...............] - ETA: 0s - loss: 3.2764e-05 - mean_absolute_error: 0.0042 88/126 [===================>..........] - ETA: 0s - loss: 3.9235e-05 - mean_absolute_error: 0.0047112/126 [=========================>....] - ETA: 0s - loss: 3.9065e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.8223e-05 - mean_absolute_error: 0.0046 - val_loss: 5.7231e-05 - val_mean_absolute_error: 0.0052
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 5.6470e-05 - mean_absolute_error: 0.0047 25/126 [====>.........................] - ETA: 0s - loss: 3.4274e-05 - mean_absolute_error: 0.0043 49/126 [==========>...................] - ETA: 0s - loss: 3.1886e-05 - mean_absolute_error: 0.0041 69/126 [===============>..............] - ETA: 0s - loss: 3.2631e-05 - mean_absolute_error: 0.0042 89/126 [====================>.........] - ETA: 0s - loss: 3.5129e-05 - mean_absolute_error: 0.0043109/126 [========================>.....] - ETA: 0s - loss: 3.6264e-05 - mean_absolute_error: 0.0044125/126 [============================>.] - ETA: 0s - loss: 3.6056e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 3ms/step - loss: 3.6000e-05 - mean_absolute_error: 0.0044 - val_loss: 5.5613e-05 - val_mean_absolute_error: 0.0051
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 1.6768e-05 - mean_absolute_error: 0.0030 17/126 [===>..........................] - ETA: 0s - loss: 2.8402e-05 - mean_absolute_error: 0.0039 36/126 [=======>......................] - ETA: 0s - loss: 3.0324e-05 - mean_absolute_error: 0.0040 54/126 [===========>..................] - ETA: 0s - loss: 3.3518e-05 - mean_absolute_error: 0.0042 72/126 [================>.............] - ETA: 0s - loss: 3.3850e-05 - mean_absolute_error: 0.0042 84/126 [===================>..........] - ETA: 0s - loss: 3.3496e-05 - mean_absolute_error: 0.0042108/126 [========================>.....] - ETA: 0s - loss: 3.4108e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.4738e-05 - mean_absolute_error: 0.0044 - val_loss: 8.5158e-05 - val_mean_absolute_error: 0.0074
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 6.9709e-05 - mean_absolute_error: 0.0071 22/126 [====>.........................] - ETA: 0s - loss: 5.2458e-05 - mean_absolute_error: 0.0058 43/126 [=========>....................] - ETA: 0s - loss: 5.3050e-05 - mean_absolute_error: 0.0058 62/126 [=============>................] - ETA: 0s - loss: 4.8682e-05 - mean_absolute_error: 0.0055 80/126 [==================>...........] - ETA: 0s - loss: 4.5467e-05 - mean_absolute_error: 0.0053101/126 [=======================>......] - ETA: 0s - loss: 4.4191e-05 - mean_absolute_error: 0.0052125/126 [============================>.] - ETA: 0s - loss: 4.4400e-05 - mean_absolute_error: 0.0052126/126 [==============================] - 0s 3ms/step - loss: 4.4303e-05 - mean_absolute_error: 0.0052 - val_loss: 7.2644e-05 - val_mean_absolute_error: 0.0066
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 5.2103e-05 - mean_absolute_error: 0.0061 26/126 [=====>........................] - ETA: 0s - loss: 3.6394e-05 - mean_absolute_error: 0.0044 52/126 [===========>..................] - ETA: 0s - loss: 3.5116e-05 - mean_absolute_error: 0.0044 75/126 [================>.............] - ETA: 0s - loss: 3.6674e-05 - mean_absolute_error: 0.0046 99/126 [======================>.......] - ETA: 0s - loss: 3.7277e-05 - mean_absolute_error: 0.0046121/126 [===========================>..] - ETA: 0s - loss: 3.7745e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7852e-05 - mean_absolute_error: 0.0046 - val_loss: 6.8651e-05 - val_mean_absolute_error: 0.0061
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 3.6716e-05 - mean_absolute_error: 0.0047 25/126 [====>.........................] - ETA: 0s - loss: 3.8027e-05 - mean_absolute_error: 0.0048 50/126 [==========>...................] - ETA: 0s - loss: 3.5615e-05 - mean_absolute_error: 0.0045 75/126 [================>.............] - ETA: 0s - loss: 3.3838e-05 - mean_absolute_error: 0.0043100/126 [======================>.......] - ETA: 0s - loss: 3.4885e-05 - mean_absolute_error: 0.0044125/126 [============================>.] - ETA: 0s - loss: 3.4815e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.4815e-05 - mean_absolute_error: 0.0044 - val_loss: 7.0471e-05 - val_mean_absolute_error: 0.0062
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 2.4848e-05 - mean_absolute_error: 0.0045 27/126 [=====>........................] - ETA: 0s - loss: 3.7936e-05 - mean_absolute_error: 0.0047 51/126 [===========>..................] - ETA: 0s - loss: 3.7341e-05 - mean_absolute_error: 0.0047 76/126 [=================>............] - ETA: 0s - loss: 3.5734e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.9160e-05 - mean_absolute_error: 0.0048122/126 [============================>.] - ETA: 0s - loss: 3.8439e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8272e-05 - mean_absolute_error: 0.0046 - val_loss: 5.4589e-05 - val_mean_absolute_error: 0.0050
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 4.9266e-05 - mean_absolute_error: 0.0056 25/126 [====>.........................] - ETA: 0s - loss: 3.5436e-05 - mean_absolute_error: 0.0046 51/126 [===========>..................] - ETA: 0s - loss: 3.6110e-05 - mean_absolute_error: 0.0045 76/126 [=================>............] - ETA: 0s - loss: 3.3745e-05 - mean_absolute_error: 0.0043101/126 [=======================>......] - ETA: 0s - loss: 3.3585e-05 - mean_absolute_error: 0.0043126/126 [==============================] - ETA: 0s - loss: 3.4484e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.4484e-05 - mean_absolute_error: 0.0043 - val_loss: 9.8837e-05 - val_mean_absolute_error: 0.0082
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 4.2361e-05 - mean_absolute_error: 0.0059 25/126 [====>.........................] - ETA: 0s - loss: 3.8144e-05 - mean_absolute_error: 0.0047 50/126 [==========>...................] - ETA: 0s - loss: 4.1980e-05 - mean_absolute_error: 0.0050 76/126 [=================>............] - ETA: 0s - loss: 4.2051e-05 - mean_absolute_error: 0.0050 99/126 [======================>.......] - ETA: 0s - loss: 3.9667e-05 - mean_absolute_error: 0.0048123/126 [============================>.] - ETA: 0s - loss: 3.8262e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8091e-05 - mean_absolute_error: 0.0047 - val_loss: 5.7778e-05 - val_mean_absolute_error: 0.0055
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 2.6428e-05 - mean_absolute_error: 0.0038 16/126 [==>...........................] - ETA: 0s - loss: 3.7355e-05 - mean_absolute_error: 0.0046 39/126 [========>.....................] - ETA: 0s - loss: 3.2225e-05 - mean_absolute_error: 0.0041 65/126 [==============>...............] - ETA: 0s - loss: 3.4967e-05 - mean_absolute_error: 0.0044 91/126 [====================>.........] - ETA: 0s - loss: 3.6334e-05 - mean_absolute_error: 0.0045116/126 [==========================>...] - ETA: 0s - loss: 3.4967e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.4523e-05 - mean_absolute_error: 0.0044 - val_loss: 5.4916e-05 - val_mean_absolute_error: 0.0050
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 1.7992e-05 - mean_absolute_error: 0.0033 24/126 [====>.........................] - ETA: 0s - loss: 3.0225e-05 - mean_absolute_error: 0.0040 48/126 [==========>...................] - ETA: 0s - loss: 3.3590e-05 - mean_absolute_error: 0.0042 73/126 [================>.............] - ETA: 0s - loss: 3.6301e-05 - mean_absolute_error: 0.0045 98/126 [======================>.......] - ETA: 0s - loss: 3.8350e-05 - mean_absolute_error: 0.0046124/126 [============================>.] - ETA: 0s - loss: 3.6786e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.6706e-05 - mean_absolute_error: 0.0045 - val_loss: 5.4361e-05 - val_mean_absolute_error: 0.0052
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 3.6892e-05 - mean_absolute_error: 0.0040 25/126 [====>.........................] - ETA: 0s - loss: 4.0795e-05 - mean_absolute_error: 0.0048 50/126 [==========>...................] - ETA: 0s - loss: 3.7609e-05 - mean_absolute_error: 0.0047 74/126 [================>.............] - ETA: 0s - loss: 3.6889e-05 - mean_absolute_error: 0.0046 99/126 [======================>.......] - ETA: 0s - loss: 3.7131e-05 - mean_absolute_error: 0.0046124/126 [============================>.] - ETA: 0s - loss: 3.5649e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.5548e-05 - mean_absolute_error: 0.0045 - val_loss: 5.6439e-05 - val_mean_absolute_error: 0.0052
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 2.9104e-05 - mean_absolute_error: 0.0043 25/126 [====>.........................] - ETA: 0s - loss: 3.5326e-05 - mean_absolute_error: 0.0045 50/126 [==========>...................] - ETA: 0s - loss: 3.8738e-05 - mean_absolute_error: 0.0048 74/126 [================>.............] - ETA: 0s - loss: 3.8829e-05 - mean_absolute_error: 0.0047 99/126 [======================>.......] - ETA: 0s - loss: 3.7138e-05 - mean_absolute_error: 0.0046121/126 [===========================>..] - ETA: 0s - loss: 3.8326e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 3ms/step - loss: 3.8322e-05 - mean_absolute_error: 0.0047 - val_loss: 5.4588e-05 - val_mean_absolute_error: 0.0050
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 2.4374e-05 - mean_absolute_error: 0.0037 20/126 [===>..........................] - ETA: 0s - loss: 4.1566e-05 - mean_absolute_error: 0.0051 42/126 [=========>....................] - ETA: 0s - loss: 4.0345e-05 - mean_absolute_error: 0.0050 67/126 [==============>...............] - ETA: 0s - loss: 3.7676e-05 - mean_absolute_error: 0.0047 92/126 [====================>.........] - ETA: 0s - loss: 3.8848e-05 - mean_absolute_error: 0.0048116/126 [==========================>...] - ETA: 0s - loss: 4.1341e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.0584e-05 - mean_absolute_error: 0.0049 - val_loss: 6.0816e-05 - val_mean_absolute_error: 0.0056
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 3.9827e-05 - mean_absolute_error: 0.0049 27/126 [=====>........................] - ETA: 0s - loss: 4.1814e-05 - mean_absolute_error: 0.0050 53/126 [===========>..................] - ETA: 0s - loss: 4.4245e-05 - mean_absolute_error: 0.0052 79/126 [=================>............] - ETA: 0s - loss: 4.6670e-05 - mean_absolute_error: 0.0054100/126 [======================>.......] - ETA: 0s - loss: 4.5664e-05 - mean_absolute_error: 0.0053119/126 [===========================>..] - ETA: 0s - loss: 4.8897e-05 - mean_absolute_error: 0.0055126/126 [==============================] - 0s 3ms/step - loss: 4.8294e-05 - mean_absolute_error: 0.0055 - val_loss: 5.5781e-05 - val_mean_absolute_error: 0.0052
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 2.3743e-05 - mean_absolute_error: 0.0036 23/126 [====>.........................] - ETA: 0s - loss: 3.3974e-05 - mean_absolute_error: 0.0046 44/126 [=========>....................] - ETA: 0s - loss: 3.6933e-05 - mean_absolute_error: 0.0048 69/126 [===============>..............] - ETA: 0s - loss: 3.3698e-05 - mean_absolute_error: 0.0044 94/126 [=====================>........] - ETA: 0s - loss: 3.5601e-05 - mean_absolute_error: 0.0045116/126 [==========================>...] - ETA: 0s - loss: 3.6031e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.7469e-05 - mean_absolute_error: 0.0047 - val_loss: 6.7757e-05 - val_mean_absolute_error: 0.0061
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 3.0958e-05 - mean_absolute_error: 0.0042 10/126 [=>............................] - ETA: 0s - loss: 3.4331e-05 - mean_absolute_error: 0.0045 22/126 [====>.........................] - ETA: 0s - loss: 4.0692e-05 - mean_absolute_error: 0.0049 38/126 [========>.....................] - ETA: 0s - loss: 3.7463e-05 - mean_absolute_error: 0.0046 54/126 [===========>..................] - ETA: 0s - loss: 3.5020e-05 - mean_absolute_error: 0.0044 68/126 [===============>..............] - ETA: 0s - loss: 3.4378e-05 - mean_absolute_error: 0.0043 85/126 [===================>..........] - ETA: 0s - loss: 3.3865e-05 - mean_absolute_error: 0.0043100/126 [======================>.......] - ETA: 0s - loss: 3.3812e-05 - mean_absolute_error: 0.0043117/126 [==========================>...] - ETA: 0s - loss: 3.4604e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 1s 4ms/step - loss: 3.4920e-05 - mean_absolute_error: 0.0044 - val_loss: 5.6434e-05 - val_mean_absolute_error: 0.0052
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 4.0155e-05 - mean_absolute_error: 0.0049 17/126 [===>..........................] - ETA: 0s - loss: 3.2548e-05 - mean_absolute_error: 0.0042 38/126 [========>.....................] - ETA: 0s - loss: 4.2391e-05 - mean_absolute_error: 0.0050 59/126 [=============>................] - ETA: 0s - loss: 3.8754e-05 - mean_absolute_error: 0.0047 82/126 [==================>...........] - ETA: 0s - loss: 3.6129e-05 - mean_absolute_error: 0.0045105/126 [========================>.....] - ETA: 0s - loss: 3.5183e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.4742e-05 - mean_absolute_error: 0.0043 - val_loss: 6.8951e-05 - val_mean_absolute_error: 0.0065
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 3.6949e-05 - mean_absolute_error: 0.0052 25/126 [====>.........................] - ETA: 0s - loss: 3.0968e-05 - mean_absolute_error: 0.0040 46/126 [=========>....................] - ETA: 0s - loss: 3.5342e-05 - mean_absolute_error: 0.0044 67/126 [==============>...............] - ETA: 0s - loss: 4.1722e-05 - mean_absolute_error: 0.0049 86/126 [===================>..........] - ETA: 0s - loss: 4.1837e-05 - mean_absolute_error: 0.0049105/126 [========================>.....] - ETA: 0s - loss: 4.0028e-05 - mean_absolute_error: 0.0048124/126 [============================>.] - ETA: 0s - loss: 3.9947e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9986e-05 - mean_absolute_error: 0.0048 - val_loss: 5.6271e-05 - val_mean_absolute_error: 0.0055

<keras.src.callbacks.History at 0x267ce11eed0>

After the training and fitting of the Machine Learning model for the United Kingdom, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for United Kingdom with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
united_kingdom_train_pred = united_kingdom_model.predict(X_united_kingdom_train).flatten()

plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4)
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Training Set Predictions vs. Observations")
plt.show()

united_kingdom_val_pred = united_kingdom_model.predict(X_united_kingdom_val).flatten()

plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Validation Set Predictions vs. Observations")
plt.show()

united_kingdom_test_pred = united_kingdom_model.predict(X_united_kingdom_test).flatten()

plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 53s 38/126 [========>.....................] - ETA: 0s  70/126 [===============>..............] - ETA: 0s103/126 [=======================>......] - ETA: 0s126/126 [==============================] - 1s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting the United Kingdom’s international currency rate with the United States.

```{python}
# Plotting United Kingdom's observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model) 
plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4)
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of United Kingdom's Dataset - Predictions vs. Observations")
plt.show()
```

Lastly, I worked on Switzerland’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Switzerland’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting Switzerland's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_switzerland_train, X_switzerland_train, y_switzerland_train = dates_switzerland[:percentile_80], X_switzerland[:percentile_80], y_switzerland[:percentile_80]
dates_switzerland_val, X_switzerland_val, y_switzerland_val = dates_switzerland[percentile_80:percentile_90], X_switzerland[percentile_80:percentile_90], y_switzerland[percentile_80:percentile_90]
dates_switzerland_test, X_switzerland_test, y_switzerland_test = dates_switzerland[percentile_90:], X_switzerland[percentile_90:], y_switzerland[percentile_90:]

plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Switzerland's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Switzerland
switzerland_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

switzerland_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

switzerland_model.fit(X_switzerland_train, y_switzerland_train, validation_data=(X_switzerland_val, y_switzerland_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:45 - loss: 1.6819 - mean_absolute_error: 1.2681 24/126 [====>.........................] - ETA: 0s - loss: 0.9068 - mean_absolute_error: 0.9060   49/126 [==========>...................] - ETA: 0s - loss: 0.4746 - mean_absolute_error: 0.5481 73/126 [================>.............] - ETA: 0s - loss: 0.3211 - mean_absolute_error: 0.3890 97/126 [======================>.......] - ETA: 0s - loss: 0.2427 - mean_absolute_error: 0.3065121/126 [===========================>..] - ETA: 0s - loss: 0.1951 - mean_absolute_error: 0.2546126/126 [==============================] - 3s 6ms/step - loss: 0.1884 - mean_absolute_error: 0.2471 - val_loss: 0.0022 - val_mean_absolute_error: 0.0460
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0022 - mean_absolute_error: 0.0412 24/126 [====>.........................] - ETA: 0s - loss: 0.0022 - mean_absolute_error: 0.0380 48/126 [==========>...................] - ETA: 0s - loss: 0.0020 - mean_absolute_error: 0.0371 71/126 [===============>..............] - ETA: 0s - loss: 0.0019 - mean_absolute_error: 0.0355 95/126 [=====================>........] - ETA: 0s - loss: 0.0017 - mean_absolute_error: 0.0335119/126 [===========================>..] - ETA: 0s - loss: 0.0015 - mean_absolute_error: 0.0314126/126 [==============================] - 0s 3ms/step - loss: 0.0015 - mean_absolute_error: 0.0309 - val_loss: 7.9961e-04 - val_mean_absolute_error: 0.0274
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 6.7342e-04 - mean_absolute_error: 0.0206 25/126 [====>.........................] - ETA: 0s - loss: 5.1624e-04 - mean_absolute_error: 0.0184 47/126 [==========>...................] - ETA: 0s - loss: 4.7608e-04 - mean_absolute_error: 0.0177 65/126 [==============>...............] - ETA: 0s - loss: 4.3186e-04 - mean_absolute_error: 0.0168 84/126 [===================>..........] - ETA: 0s - loss: 4.0156e-04 - mean_absolute_error: 0.0162104/126 [=======================>......] - ETA: 0s - loss: 3.7081e-04 - mean_absolute_error: 0.0155125/126 [============================>.] - ETA: 0s - loss: 3.5072e-04 - mean_absolute_error: 0.0148126/126 [==============================] - 0s 3ms/step - loss: 3.4986e-04 - mean_absolute_error: 0.0148 - val_loss: 8.2133e-05 - val_mean_absolute_error: 0.0074
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 1.8057e-04 - mean_absolute_error: 0.0115 20/126 [===>..........................] - ETA: 0s - loss: 1.9068e-04 - mean_absolute_error: 0.0103 39/126 [========>.....................] - ETA: 0s - loss: 1.8505e-04 - mean_absolute_error: 0.0104 58/126 [============>.................] - ETA: 0s - loss: 1.7264e-04 - mean_absolute_error: 0.0101 79/126 [=================>............] - ETA: 0s - loss: 1.7490e-04 - mean_absolute_error: 0.0100101/126 [=======================>......] - ETA: 0s - loss: 1.7096e-04 - mean_absolute_error: 0.0100113/126 [=========================>....] - ETA: 0s - loss: 1.6842e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 3ms/step - loss: 1.6606e-04 - mean_absolute_error: 0.0099 - val_loss: 6.0170e-05 - val_mean_absolute_error: 0.0063
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 1.4307e-04 - mean_absolute_error: 0.0095 21/126 [====>.........................] - ETA: 0s - loss: 1.5375e-04 - mean_absolute_error: 0.0095 31/126 [======>.......................] - ETA: 0s - loss: 1.5400e-04 - mean_absolute_error: 0.0095 49/126 [==========>...................] - ETA: 0s - loss: 1.5695e-04 - mean_absolute_error: 0.0096 71/126 [===============>..............] - ETA: 0s - loss: 1.6633e-04 - mean_absolute_error: 0.0096 93/126 [=====================>........] - ETA: 0s - loss: 1.6085e-04 - mean_absolute_error: 0.0095115/126 [==========================>...] - ETA: 0s - loss: 1.5628e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 3ms/step - loss: 1.5738e-04 - mean_absolute_error: 0.0095 - val_loss: 5.5257e-05 - val_mean_absolute_error: 0.0060
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 1.2232e-04 - mean_absolute_error: 0.0080 23/126 [====>.........................] - ETA: 0s - loss: 1.7144e-04 - mean_absolute_error: 0.0095 44/126 [=========>....................] - ETA: 0s - loss: 1.5983e-04 - mean_absolute_error: 0.0094 67/126 [==============>...............] - ETA: 0s - loss: 1.5806e-04 - mean_absolute_error: 0.0094 89/126 [====================>.........] - ETA: 0s - loss: 1.5869e-04 - mean_absolute_error: 0.0094110/126 [=========================>....] - ETA: 0s - loss: 1.5738e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 3ms/step - loss: 1.5559e-04 - mean_absolute_error: 0.0094 - val_loss: 4.9763e-05 - val_mean_absolute_error: 0.0055
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 1.2799e-04 - mean_absolute_error: 0.0092 25/126 [====>.........................] - ETA: 0s - loss: 1.6585e-04 - mean_absolute_error: 0.0092 49/126 [==========>...................] - ETA: 0s - loss: 1.5974e-04 - mean_absolute_error: 0.0094 73/126 [================>.............] - ETA: 0s - loss: 1.5161e-04 - mean_absolute_error: 0.0093 97/126 [======================>.......] - ETA: 0s - loss: 1.4999e-04 - mean_absolute_error: 0.0092121/126 [===========================>..] - ETA: 0s - loss: 1.5183e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5317e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9488e-05 - val_mean_absolute_error: 0.0055
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 6.1740e-05 - mean_absolute_error: 0.0064 26/126 [=====>........................] - ETA: 0s - loss: 1.3576e-04 - mean_absolute_error: 0.0090 51/126 [===========>..................] - ETA: 0s - loss: 1.5536e-04 - mean_absolute_error: 0.0092 71/126 [===============>..............] - ETA: 0s - loss: 1.5091e-04 - mean_absolute_error: 0.0091 91/126 [====================>.........] - ETA: 0s - loss: 1.5419e-04 - mean_absolute_error: 0.0092112/126 [=========================>....] - ETA: 0s - loss: 1.5418e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 3ms/step - loss: 1.5320e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9966e-05 - val_mean_absolute_error: 0.0055
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 1.1423e-04 - mean_absolute_error: 0.0080 17/126 [===>..........................] - ETA: 0s - loss: 1.3566e-04 - mean_absolute_error: 0.0090 35/126 [=======>......................] - ETA: 0s - loss: 1.4462e-04 - mean_absolute_error: 0.0093 52/126 [===========>..................] - ETA: 0s - loss: 1.4276e-04 - mean_absolute_error: 0.0092 73/126 [================>.............] - ETA: 0s - loss: 1.3841e-04 - mean_absolute_error: 0.0091 92/126 [====================>.........] - ETA: 0s - loss: 1.3595e-04 - mean_absolute_error: 0.0090112/126 [=========================>....] - ETA: 0s - loss: 1.4610e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.5380e-04 - mean_absolute_error: 0.0093 - val_loss: 7.8283e-05 - val_mean_absolute_error: 0.0072
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 3.5869e-04 - mean_absolute_error: 0.0149 21/126 [====>.........................] - ETA: 0s - loss: 1.8264e-04 - mean_absolute_error: 0.0098 40/126 [========>.....................] - ETA: 0s - loss: 1.6739e-04 - mean_absolute_error: 0.0095 58/126 [============>.................] - ETA: 0s - loss: 1.5627e-04 - mean_absolute_error: 0.0093 78/126 [=================>............] - ETA: 0s - loss: 1.6403e-04 - mean_absolute_error: 0.0094101/126 [=======================>......] - ETA: 0s - loss: 1.6065e-04 - mean_absolute_error: 0.0094126/126 [==============================] - ETA: 0s - loss: 1.5658e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 3ms/step - loss: 1.5658e-04 - mean_absolute_error: 0.0094 - val_loss: 5.7266e-05 - val_mean_absolute_error: 0.0061
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 9.8787e-05 - mean_absolute_error: 0.0085 26/126 [=====>........................] - ETA: 0s - loss: 1.7702e-04 - mean_absolute_error: 0.0097 50/126 [==========>...................] - ETA: 0s - loss: 1.5843e-04 - mean_absolute_error: 0.0094 75/126 [================>.............] - ETA: 0s - loss: 1.4754e-04 - mean_absolute_error: 0.0092 99/126 [======================>.......] - ETA: 0s - loss: 1.5128e-04 - mean_absolute_error: 0.0092121/126 [===========================>..] - ETA: 0s - loss: 1.5178e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5201e-04 - mean_absolute_error: 0.0093 - val_loss: 5.1767e-05 - val_mean_absolute_error: 0.0057
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 1.7329e-04 - mean_absolute_error: 0.0104 25/126 [====>.........................] - ETA: 0s - loss: 1.3843e-04 - mean_absolute_error: 0.0091 49/126 [==========>...................] - ETA: 0s - loss: 1.4686e-04 - mean_absolute_error: 0.0093 73/126 [================>.............] - ETA: 0s - loss: 1.4693e-04 - mean_absolute_error: 0.0093 95/126 [=====================>........] - ETA: 0s - loss: 1.5772e-04 - mean_absolute_error: 0.0094119/126 [===========================>..] - ETA: 0s - loss: 1.5502e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5434e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9384e-05 - val_mean_absolute_error: 0.0054
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 1.5101e-04 - mean_absolute_error: 0.0102 27/126 [=====>........................] - ETA: 0s - loss: 1.4302e-04 - mean_absolute_error: 0.0091 51/126 [===========>..................] - ETA: 0s - loss: 1.4489e-04 - mean_absolute_error: 0.0092 73/126 [================>.............] - ETA: 0s - loss: 1.5108e-04 - mean_absolute_error: 0.0092 87/126 [===================>..........] - ETA: 0s - loss: 1.5192e-04 - mean_absolute_error: 0.0093103/126 [=======================>......] - ETA: 0s - loss: 1.5517e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 3ms/step - loss: 1.5419e-04 - mean_absolute_error: 0.0093 - val_loss: 6.2226e-05 - val_mean_absolute_error: 0.0064
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 1.6932e-04 - mean_absolute_error: 0.0100 23/126 [====>.........................] - ETA: 0s - loss: 1.5979e-04 - mean_absolute_error: 0.0095 46/126 [=========>....................] - ETA: 0s - loss: 1.6164e-04 - mean_absolute_error: 0.0092 69/126 [===============>..............] - ETA: 0s - loss: 1.5919e-04 - mean_absolute_error: 0.0092 93/126 [=====================>........] - ETA: 0s - loss: 1.5662e-04 - mean_absolute_error: 0.0092117/126 [==========================>...] - ETA: 0s - loss: 1.5284e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.5226e-04 - mean_absolute_error: 0.0092 - val_loss: 5.3605e-05 - val_mean_absolute_error: 0.0058
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 8.6882e-05 - mean_absolute_error: 0.0062 17/126 [===>..........................] - ETA: 0s - loss: 1.6052e-04 - mean_absolute_error: 0.0095 38/126 [========>.....................] - ETA: 0s - loss: 1.7829e-04 - mean_absolute_error: 0.0100 63/126 [==============>...............] - ETA: 0s - loss: 1.7313e-04 - mean_absolute_error: 0.0100 78/126 [=================>............] - ETA: 0s - loss: 1.6628e-04 - mean_absolute_error: 0.0098102/126 [=======================>......] - ETA: 0s - loss: 1.5899e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 3ms/step - loss: 1.6105e-04 - mean_absolute_error: 0.0095 - val_loss: 4.9850e-05 - val_mean_absolute_error: 0.0055
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 1.0756e-04 - mean_absolute_error: 0.0085 28/126 [=====>........................] - ETA: 0s - loss: 1.4092e-04 - mean_absolute_error: 0.0091 55/126 [============>.................] - ETA: 0s - loss: 1.5259e-04 - mean_absolute_error: 0.0093 79/126 [=================>............] - ETA: 0s - loss: 1.5604e-04 - mean_absolute_error: 0.0094104/126 [=======================>......] - ETA: 0s - loss: 1.5138e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5317e-04 - mean_absolute_error: 0.0093 - val_loss: 8.8895e-05 - val_mean_absolute_error: 0.0078
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 2.3664e-04 - mean_absolute_error: 0.0123 26/126 [=====>........................] - ETA: 0s - loss: 1.7019e-04 - mean_absolute_error: 0.0096 52/126 [===========>..................] - ETA: 0s - loss: 1.5050e-04 - mean_absolute_error: 0.0092 77/126 [=================>............] - ETA: 0s - loss: 1.6112e-04 - mean_absolute_error: 0.0093 90/126 [====================>.........] - ETA: 0s - loss: 1.5813e-04 - mean_absolute_error: 0.0093113/126 [=========================>....] - ETA: 0s - loss: 1.5375e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.5120e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9461e-05 - val_mean_absolute_error: 0.0054
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 1.3973e-04 - mean_absolute_error: 0.0095 25/126 [====>.........................] - ETA: 0s - loss: 1.5610e-04 - mean_absolute_error: 0.0090 46/126 [=========>....................] - ETA: 0s - loss: 1.5028e-04 - mean_absolute_error: 0.0090 70/126 [===============>..............] - ETA: 0s - loss: 1.4773e-04 - mean_absolute_error: 0.0090 92/126 [====================>.........] - ETA: 0s - loss: 1.4956e-04 - mean_absolute_error: 0.0091115/126 [==========================>...] - ETA: 0s - loss: 1.5171e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 3ms/step - loss: 1.5731e-04 - mean_absolute_error: 0.0094 - val_loss: 6.1922e-05 - val_mean_absolute_error: 0.0063
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 1.2546e-04 - mean_absolute_error: 0.0089 26/126 [=====>........................] - ETA: 0s - loss: 1.3474e-04 - mean_absolute_error: 0.0089 49/126 [==========>...................] - ETA: 0s - loss: 1.4297e-04 - mean_absolute_error: 0.0091 73/126 [================>.............] - ETA: 0s - loss: 1.4847e-04 - mean_absolute_error: 0.0091 95/126 [=====================>........] - ETA: 0s - loss: 1.5434e-04 - mean_absolute_error: 0.0092118/126 [===========================>..] - ETA: 0s - loss: 1.5217e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.5128e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9411e-05 - val_mean_absolute_error: 0.0054
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 8.6509e-05 - mean_absolute_error: 0.0076 26/126 [=====>........................] - ETA: 0s - loss: 1.3878e-04 - mean_absolute_error: 0.0091 49/126 [==========>...................] - ETA: 0s - loss: 1.3724e-04 - mean_absolute_error: 0.0090 73/126 [================>.............] - ETA: 0s - loss: 1.3533e-04 - mean_absolute_error: 0.0089 96/126 [=====================>........] - ETA: 0s - loss: 1.4441e-04 - mean_absolute_error: 0.0091121/126 [===========================>..] - ETA: 0s - loss: 1.5114e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5032e-04 - mean_absolute_error: 0.0092 - val_loss: 6.4380e-05 - val_mean_absolute_error: 0.0065
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 1.7553e-04 - mean_absolute_error: 0.0100 26/126 [=====>........................] - ETA: 0s - loss: 1.4326e-04 - mean_absolute_error: 0.0094 51/126 [===========>..................] - ETA: 0s - loss: 1.5995e-04 - mean_absolute_error: 0.0096 76/126 [=================>............] - ETA: 0s - loss: 1.5439e-04 - mean_absolute_error: 0.0094103/126 [=======================>......] - ETA: 0s - loss: 1.4946e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5619e-04 - mean_absolute_error: 0.0094 - val_loss: 5.9961e-05 - val_mean_absolute_error: 0.0062
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 2.0058e-04 - mean_absolute_error: 0.0115 27/126 [=====>........................] - ETA: 0s - loss: 1.4516e-04 - mean_absolute_error: 0.0091 52/126 [===========>..................] - ETA: 0s - loss: 1.4161e-04 - mean_absolute_error: 0.0090 76/126 [=================>............] - ETA: 0s - loss: 1.5346e-04 - mean_absolute_error: 0.0093 99/126 [======================>.......] - ETA: 0s - loss: 1.5240e-04 - mean_absolute_error: 0.0093124/126 [============================>.] - ETA: 0s - loss: 1.5640e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5635e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9667e-05 - val_mean_absolute_error: 0.0054
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 1.1877e-04 - mean_absolute_error: 0.0089 27/126 [=====>........................] - ETA: 0s - loss: 1.4247e-04 - mean_absolute_error: 0.0091 52/126 [===========>..................] - ETA: 0s - loss: 1.5644e-04 - mean_absolute_error: 0.0093 77/126 [=================>............] - ETA: 0s - loss: 1.5414e-04 - mean_absolute_error: 0.0092102/126 [=======================>......] - ETA: 0s - loss: 1.5273e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5280e-04 - mean_absolute_error: 0.0092 - val_loss: 5.1789e-05 - val_mean_absolute_error: 0.0057
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 1.0095e-04 - mean_absolute_error: 0.0085 25/126 [====>.........................] - ETA: 0s - loss: 1.4332e-04 - mean_absolute_error: 0.0093 49/126 [==========>...................] - ETA: 0s - loss: 1.4638e-04 - mean_absolute_error: 0.0092 74/126 [================>.............] - ETA: 0s - loss: 1.4841e-04 - mean_absolute_error: 0.0091 99/126 [======================>.......] - ETA: 0s - loss: 1.5087e-04 - mean_absolute_error: 0.0091125/126 [============================>.] - ETA: 0s - loss: 1.4865e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4849e-04 - mean_absolute_error: 0.0091 - val_loss: 7.6408e-05 - val_mean_absolute_error: 0.0071
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 1.1696e-04 - mean_absolute_error: 0.0093 25/126 [====>.........................] - ETA: 0s - loss: 1.6263e-04 - mean_absolute_error: 0.0095 51/126 [===========>..................] - ETA: 0s - loss: 1.5276e-04 - mean_absolute_error: 0.0093 76/126 [=================>............] - ETA: 0s - loss: 1.5566e-04 - mean_absolute_error: 0.0093101/126 [=======================>......] - ETA: 0s - loss: 1.5687e-04 - mean_absolute_error: 0.0094124/126 [============================>.] - ETA: 0s - loss: 1.5495e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5551e-04 - mean_absolute_error: 0.0094 - val_loss: 5.0872e-05 - val_mean_absolute_error: 0.0056
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 1.1982e-04 - mean_absolute_error: 0.0088 27/126 [=====>........................] - ETA: 0s - loss: 1.4887e-04 - mean_absolute_error: 0.0088 51/126 [===========>..................] - ETA: 0s - loss: 1.6069e-04 - mean_absolute_error: 0.0091 77/126 [=================>............] - ETA: 0s - loss: 1.5944e-04 - mean_absolute_error: 0.0092103/126 [=======================>......] - ETA: 0s - loss: 1.5250e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.5250e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9499e-05 - val_mean_absolute_error: 0.0054
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 8.2325e-05 - mean_absolute_error: 0.0066 27/126 [=====>........................] - ETA: 0s - loss: 1.6456e-04 - mean_absolute_error: 0.0095 53/126 [===========>..................] - ETA: 0s - loss: 1.5389e-04 - mean_absolute_error: 0.0094 79/126 [=================>............] - ETA: 0s - loss: 1.4968e-04 - mean_absolute_error: 0.0092104/126 [=======================>......] - ETA: 0s - loss: 1.5023e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4925e-04 - mean_absolute_error: 0.0091 - val_loss: 8.7145e-05 - val_mean_absolute_error: 0.0077
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 2.8463e-04 - mean_absolute_error: 0.0136 27/126 [=====>........................] - ETA: 0s - loss: 1.5926e-04 - mean_absolute_error: 0.0092 53/126 [===========>..................] - ETA: 0s - loss: 1.4799e-04 - mean_absolute_error: 0.0089 77/126 [=================>............] - ETA: 0s - loss: 1.5745e-04 - mean_absolute_error: 0.0092101/126 [=======================>......] - ETA: 0s - loss: 1.6035e-04 - mean_absolute_error: 0.0094124/126 [============================>.] - ETA: 0s - loss: 1.5645e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5649e-04 - mean_absolute_error: 0.0094 - val_loss: 4.9577e-05 - val_mean_absolute_error: 0.0054
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 8.3198e-05 - mean_absolute_error: 0.0069 27/126 [=====>........................] - ETA: 0s - loss: 1.5156e-04 - mean_absolute_error: 0.0091 52/126 [===========>..................] - ETA: 0s - loss: 1.5002e-04 - mean_absolute_error: 0.0092 78/126 [=================>............] - ETA: 0s - loss: 1.5339e-04 - mean_absolute_error: 0.0093103/126 [=======================>......] - ETA: 0s - loss: 1.5713e-04 - mean_absolute_error: 0.0095122/126 [============================>.] - ETA: 0s - loss: 1.5801e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 3ms/step - loss: 1.5670e-04 - mean_absolute_error: 0.0094 - val_loss: 5.0258e-05 - val_mean_absolute_error: 0.0055
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 1.7082e-04 - mean_absolute_error: 0.0106 23/126 [====>.........................] - ETA: 0s - loss: 1.3145e-04 - mean_absolute_error: 0.0087 46/126 [=========>....................] - ETA: 0s - loss: 1.3430e-04 - mean_absolute_error: 0.0090 69/126 [===============>..............] - ETA: 0s - loss: 1.4559e-04 - mean_absolute_error: 0.0090 93/126 [=====================>........] - ETA: 0s - loss: 1.4551e-04 - mean_absolute_error: 0.0091116/126 [==========================>...] - ETA: 0s - loss: 1.4627e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 3ms/step - loss: 1.4747e-04 - mean_absolute_error: 0.0091 - val_loss: 7.1410e-05 - val_mean_absolute_error: 0.0069
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 2.2126e-04 - mean_absolute_error: 0.0122 26/126 [=====>........................] - ETA: 0s - loss: 1.9231e-04 - mean_absolute_error: 0.0105 49/126 [==========>...................] - ETA: 0s - loss: 1.6433e-04 - mean_absolute_error: 0.0096 72/126 [================>.............] - ETA: 0s - loss: 1.5899e-04 - mean_absolute_error: 0.0095 93/126 [=====================>........] - ETA: 0s - loss: 1.5853e-04 - mean_absolute_error: 0.0095117/126 [==========================>...] - ETA: 0s - loss: 1.6433e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 3ms/step - loss: 1.6761e-04 - mean_absolute_error: 0.0097 - val_loss: 1.1944e-04 - val_mean_absolute_error: 0.0091
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 4.1595e-04 - mean_absolute_error: 0.0150 23/126 [====>.........................] - ETA: 0s - loss: 2.0445e-04 - mean_absolute_error: 0.0112 47/126 [==========>...................] - ETA: 0s - loss: 1.9609e-04 - mean_absolute_error: 0.0106 69/126 [===============>..............] - ETA: 0s - loss: 1.8966e-04 - mean_absolute_error: 0.0103 90/126 [====================>.........] - ETA: 0s - loss: 1.8235e-04 - mean_absolute_error: 0.0101114/126 [==========================>...] - ETA: 0s - loss: 1.7209e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 3ms/step - loss: 1.6835e-04 - mean_absolute_error: 0.0097 - val_loss: 5.4123e-05 - val_mean_absolute_error: 0.0059
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 9.2544e-05 - mean_absolute_error: 0.0078 24/126 [====>.........................] - ETA: 0s - loss: 1.3984e-04 - mean_absolute_error: 0.0087 48/126 [==========>...................] - ETA: 0s - loss: 1.3294e-04 - mean_absolute_error: 0.0086 71/126 [===============>..............] - ETA: 0s - loss: 1.3711e-04 - mean_absolute_error: 0.0088 93/126 [=====================>........] - ETA: 0s - loss: 1.4007e-04 - mean_absolute_error: 0.0089116/126 [==========================>...] - ETA: 0s - loss: 1.5118e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.5437e-04 - mean_absolute_error: 0.0093 - val_loss: 1.3613e-04 - val_mean_absolute_error: 0.0100
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 2.5579e-04 - mean_absolute_error: 0.0134 26/126 [=====>........................] - ETA: 0s - loss: 1.5256e-04 - mean_absolute_error: 0.0096 49/126 [==========>...................] - ETA: 0s - loss: 1.8147e-04 - mean_absolute_error: 0.0104 72/126 [================>.............] - ETA: 0s - loss: 1.6819e-04 - mean_absolute_error: 0.0099 95/126 [=====================>........] - ETA: 0s - loss: 1.6883e-04 - mean_absolute_error: 0.0099119/126 [===========================>..] - ETA: 0s - loss: 1.6319e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 2ms/step - loss: 1.6353e-04 - mean_absolute_error: 0.0096 - val_loss: 5.5964e-05 - val_mean_absolute_error: 0.0060
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 1.3573e-04 - mean_absolute_error: 0.0094 28/126 [=====>........................] - ETA: 0s - loss: 1.6066e-04 - mean_absolute_error: 0.0094 53/126 [===========>..................] - ETA: 0s - loss: 1.6149e-04 - mean_absolute_error: 0.0096 79/126 [=================>............] - ETA: 0s - loss: 1.7239e-04 - mean_absolute_error: 0.0099105/126 [========================>.....] - ETA: 0s - loss: 2.0179e-04 - mean_absolute_error: 0.0107126/126 [==============================] - 0s 2ms/step - loss: 1.9724e-04 - mean_absolute_error: 0.0106 - val_loss: 1.0345e-04 - val_mean_absolute_error: 0.0085
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 1.7825e-04 - mean_absolute_error: 0.0111 27/126 [=====>........................] - ETA: 0s - loss: 1.6740e-04 - mean_absolute_error: 0.0096 53/126 [===========>..................] - ETA: 0s - loss: 1.9193e-04 - mean_absolute_error: 0.0103 79/126 [=================>............] - ETA: 0s - loss: 2.1078e-04 - mean_absolute_error: 0.0110104/126 [=======================>......] - ETA: 0s - loss: 2.0204e-04 - mean_absolute_error: 0.0107126/126 [==============================] - 0s 2ms/step - loss: 1.9138e-04 - mean_absolute_error: 0.0104 - val_loss: 6.0112e-05 - val_mean_absolute_error: 0.0062
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 1.4289e-04 - mean_absolute_error: 0.0093 25/126 [====>.........................] - ETA: 0s - loss: 1.6312e-04 - mean_absolute_error: 0.0101 51/126 [===========>..................] - ETA: 0s - loss: 1.8001e-04 - mean_absolute_error: 0.0102 77/126 [=================>............] - ETA: 0s - loss: 1.6778e-04 - mean_absolute_error: 0.0099103/126 [=======================>......] - ETA: 0s - loss: 1.7105e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6776e-04 - mean_absolute_error: 0.0098 - val_loss: 4.8907e-05 - val_mean_absolute_error: 0.0053
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 5.9728e-05 - mean_absolute_error: 0.0060 27/126 [=====>........................] - ETA: 0s - loss: 1.4721e-04 - mean_absolute_error: 0.0092 53/126 [===========>..................] - ETA: 0s - loss: 1.6913e-04 - mean_absolute_error: 0.0097 79/126 [=================>............] - ETA: 0s - loss: 1.7722e-04 - mean_absolute_error: 0.0099104/126 [=======================>......] - ETA: 0s - loss: 1.7003e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6186e-04 - mean_absolute_error: 0.0095 - val_loss: 4.9067e-05 - val_mean_absolute_error: 0.0053
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 1.6943e-04 - mean_absolute_error: 0.0102 26/126 [=====>........................] - ETA: 0s - loss: 1.3314e-04 - mean_absolute_error: 0.0088 53/126 [===========>..................] - ETA: 0s - loss: 1.3525e-04 - mean_absolute_error: 0.0089 79/126 [=================>............] - ETA: 0s - loss: 1.3863e-04 - mean_absolute_error: 0.0090105/126 [========================>.....] - ETA: 0s - loss: 1.4448e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4688e-04 - mean_absolute_error: 0.0090 - val_loss: 4.9877e-05 - val_mean_absolute_error: 0.0055
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 1.3049e-04 - mean_absolute_error: 0.0089 27/126 [=====>........................] - ETA: 0s - loss: 1.9727e-04 - mean_absolute_error: 0.0104 53/126 [===========>..................] - ETA: 0s - loss: 1.7580e-04 - mean_absolute_error: 0.0098 77/126 [=================>............] - ETA: 0s - loss: 1.7375e-04 - mean_absolute_error: 0.0098102/126 [=======================>......] - ETA: 0s - loss: 1.7245e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6700e-04 - mean_absolute_error: 0.0097 - val_loss: 5.4270e-05 - val_mean_absolute_error: 0.0059
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 6.8300e-05 - mean_absolute_error: 0.0067 28/126 [=====>........................] - ETA: 0s - loss: 1.3868e-04 - mean_absolute_error: 0.0085 54/126 [===========>..................] - ETA: 0s - loss: 1.4757e-04 - mean_absolute_error: 0.0087 80/126 [==================>...........] - ETA: 0s - loss: 1.4710e-04 - mean_absolute_error: 0.0089106/126 [========================>.....] - ETA: 0s - loss: 1.5989e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.6169e-04 - mean_absolute_error: 0.0095 - val_loss: 6.1827e-05 - val_mean_absolute_error: 0.0063
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 1.6029e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.3332e-04 - mean_absolute_error: 0.0088 52/126 [===========>..................] - ETA: 0s - loss: 1.3810e-04 - mean_absolute_error: 0.0090 78/126 [=================>............] - ETA: 0s - loss: 1.5181e-04 - mean_absolute_error: 0.0092103/126 [=======================>......] - ETA: 0s - loss: 1.5045e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5063e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9859e-05 - val_mean_absolute_error: 0.0055
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 9.8770e-05 - mean_absolute_error: 0.0082 26/126 [=====>........................] - ETA: 0s - loss: 1.9548e-04 - mean_absolute_error: 0.0110 52/126 [===========>..................] - ETA: 0s - loss: 1.8452e-04 - mean_absolute_error: 0.0107 77/126 [=================>............] - ETA: 0s - loss: 1.7184e-04 - mean_absolute_error: 0.0100102/126 [=======================>......] - ETA: 0s - loss: 1.6851e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6601e-04 - mean_absolute_error: 0.0097 - val_loss: 5.5557e-05 - val_mean_absolute_error: 0.0059
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 7.0374e-05 - mean_absolute_error: 0.0070 27/126 [=====>........................] - ETA: 0s - loss: 2.2635e-04 - mean_absolute_error: 0.0113 53/126 [===========>..................] - ETA: 0s - loss: 2.0096e-04 - mean_absolute_error: 0.0108 78/126 [=================>............] - ETA: 0s - loss: 1.8049e-04 - mean_absolute_error: 0.0100102/126 [=======================>......] - ETA: 0s - loss: 1.7163e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6716e-04 - mean_absolute_error: 0.0097 - val_loss: 5.2272e-05 - val_mean_absolute_error: 0.0057
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 1.2917e-04 - mean_absolute_error: 0.0091 27/126 [=====>........................] - ETA: 0s - loss: 1.3665e-04 - mean_absolute_error: 0.0092 52/126 [===========>..................] - ETA: 0s - loss: 1.6185e-04 - mean_absolute_error: 0.0096 77/126 [=================>............] - ETA: 0s - loss: 1.6801e-04 - mean_absolute_error: 0.0096100/126 [======================>.......] - ETA: 0s - loss: 1.6035e-04 - mean_absolute_error: 0.0095125/126 [============================>.] - ETA: 0s - loss: 1.5596e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5582e-04 - mean_absolute_error: 0.0094 - val_loss: 5.9029e-05 - val_mean_absolute_error: 0.0061
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 1.3889e-04 - mean_absolute_error: 0.0087 27/126 [=====>........................] - ETA: 0s - loss: 1.4997e-04 - mean_absolute_error: 0.0093 54/126 [===========>..................] - ETA: 0s - loss: 1.4902e-04 - mean_absolute_error: 0.0093 80/126 [==================>...........] - ETA: 0s - loss: 1.5101e-04 - mean_absolute_error: 0.0093106/126 [========================>.....] - ETA: 0s - loss: 1.5987e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5552e-04 - mean_absolute_error: 0.0094 - val_loss: 1.3149e-04 - val_mean_absolute_error: 0.0098
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 2.7085e-04 - mean_absolute_error: 0.0136 27/126 [=====>........................] - ETA: 0s - loss: 1.8360e-04 - mean_absolute_error: 0.0100 53/126 [===========>..................] - ETA: 0s - loss: 1.5797e-04 - mean_absolute_error: 0.0093 76/126 [=================>............] - ETA: 0s - loss: 1.5186e-04 - mean_absolute_error: 0.0093102/126 [=======================>......] - ETA: 0s - loss: 1.5252e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5604e-04 - mean_absolute_error: 0.0094 - val_loss: 7.5431e-05 - val_mean_absolute_error: 0.0071
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 1.6342e-04 - mean_absolute_error: 0.0095 28/126 [=====>........................] - ETA: 0s - loss: 2.2685e-04 - mean_absolute_error: 0.0114 53/126 [===========>..................] - ETA: 0s - loss: 1.8845e-04 - mean_absolute_error: 0.0105 80/126 [==================>...........] - ETA: 0s - loss: 1.7332e-04 - mean_absolute_error: 0.0101105/126 [========================>.....] - ETA: 0s - loss: 1.6333e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6459e-04 - mean_absolute_error: 0.0097 - val_loss: 5.2577e-05 - val_mean_absolute_error: 0.0058
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 1.4508e-04 - mean_absolute_error: 0.0088 27/126 [=====>........................] - ETA: 0s - loss: 1.3540e-04 - mean_absolute_error: 0.0089 52/126 [===========>..................] - ETA: 0s - loss: 1.7832e-04 - mean_absolute_error: 0.0101 78/126 [=================>............] - ETA: 0s - loss: 1.9254e-04 - mean_absolute_error: 0.0105104/126 [=======================>......] - ETA: 0s - loss: 1.8837e-04 - mean_absolute_error: 0.0105126/126 [==============================] - 0s 2ms/step - loss: 1.7752e-04 - mean_absolute_error: 0.0101 - val_loss: 6.1680e-05 - val_mean_absolute_error: 0.0063
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 9.2102e-05 - mean_absolute_error: 0.0071 27/126 [=====>........................] - ETA: 0s - loss: 1.8418e-04 - mean_absolute_error: 0.0101 52/126 [===========>..................] - ETA: 0s - loss: 1.6031e-04 - mean_absolute_error: 0.0095 78/126 [=================>............] - ETA: 0s - loss: 1.5134e-04 - mean_absolute_error: 0.0092104/126 [=======================>......] - ETA: 0s - loss: 1.4773e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4235e-04 - mean_absolute_error: 0.0089 - val_loss: 5.0935e-05 - val_mean_absolute_error: 0.0057
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 1.6447e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.5535e-04 - mean_absolute_error: 0.0096 53/126 [===========>..................] - ETA: 0s - loss: 1.6087e-04 - mean_absolute_error: 0.0095 79/126 [=================>............] - ETA: 0s - loss: 1.6478e-04 - mean_absolute_error: 0.0095104/126 [=======================>......] - ETA: 0s - loss: 1.6173e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 2ms/step - loss: 1.5868e-04 - mean_absolute_error: 0.0095 - val_loss: 4.9898e-05 - val_mean_absolute_error: 0.0056
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 9.6459e-05 - mean_absolute_error: 0.0080 27/126 [=====>........................] - ETA: 0s - loss: 1.6567e-04 - mean_absolute_error: 0.0093 53/126 [===========>..................] - ETA: 0s - loss: 1.5696e-04 - mean_absolute_error: 0.0093 79/126 [=================>............] - ETA: 0s - loss: 1.5827e-04 - mean_absolute_error: 0.0094102/126 [=======================>......] - ETA: 0s - loss: 1.5258e-04 - mean_absolute_error: 0.0093126/126 [==============================] - ETA: 0s - loss: 1.5198e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5198e-04 - mean_absolute_error: 0.0092 - val_loss: 4.7802e-05 - val_mean_absolute_error: 0.0052
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 5.8763e-05 - mean_absolute_error: 0.0061 28/126 [=====>........................] - ETA: 0s - loss: 1.5458e-04 - mean_absolute_error: 0.0090 54/126 [===========>..................] - ETA: 0s - loss: 2.0052e-04 - mean_absolute_error: 0.0104 80/126 [==================>...........] - ETA: 0s - loss: 2.0780e-04 - mean_absolute_error: 0.0109106/126 [========================>.....] - ETA: 0s - loss: 1.9086e-04 - mean_absolute_error: 0.0105126/126 [==============================] - 0s 2ms/step - loss: 1.8270e-04 - mean_absolute_error: 0.0102 - val_loss: 5.5754e-05 - val_mean_absolute_error: 0.0060
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 1.7081e-04 - mean_absolute_error: 0.0098 27/126 [=====>........................] - ETA: 0s - loss: 1.7638e-04 - mean_absolute_error: 0.0096 52/126 [===========>..................] - ETA: 0s - loss: 1.5717e-04 - mean_absolute_error: 0.0091 78/126 [=================>............] - ETA: 0s - loss: 1.5218e-04 - mean_absolute_error: 0.0091104/126 [=======================>......] - ETA: 0s - loss: 1.4958e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.5485e-04 - mean_absolute_error: 0.0093 - val_loss: 1.7321e-04 - val_mean_absolute_error: 0.0115
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 4.7579e-04 - mean_absolute_error: 0.0187 27/126 [=====>........................] - ETA: 0s - loss: 1.6401e-04 - mean_absolute_error: 0.0094 53/126 [===========>..................] - ETA: 0s - loss: 1.4177e-04 - mean_absolute_error: 0.0089 79/126 [=================>............] - ETA: 0s - loss: 1.5187e-04 - mean_absolute_error: 0.0093104/126 [=======================>......] - ETA: 0s - loss: 1.4901e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5286e-04 - mean_absolute_error: 0.0092 - val_loss: 9.6741e-05 - val_mean_absolute_error: 0.0082
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 1.6132e-04 - mean_absolute_error: 0.0109 27/126 [=====>........................] - ETA: 0s - loss: 1.5188e-04 - mean_absolute_error: 0.0093 54/126 [===========>..................] - ETA: 0s - loss: 1.4022e-04 - mean_absolute_error: 0.0089 80/126 [==================>...........] - ETA: 0s - loss: 1.6110e-04 - mean_absolute_error: 0.0094107/126 [========================>.....] - ETA: 0s - loss: 1.5487e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5126e-04 - mean_absolute_error: 0.0092 - val_loss: 4.7719e-05 - val_mean_absolute_error: 0.0053
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 1.5305e-04 - mean_absolute_error: 0.0098 26/126 [=====>........................] - ETA: 0s - loss: 1.6457e-04 - mean_absolute_error: 0.0094 48/126 [==========>...................] - ETA: 0s - loss: 1.4681e-04 - mean_absolute_error: 0.0088 69/126 [===============>..............] - ETA: 0s - loss: 1.4626e-04 - mean_absolute_error: 0.0089 90/126 [====================>.........] - ETA: 0s - loss: 1.4364e-04 - mean_absolute_error: 0.0089113/126 [=========================>....] - ETA: 0s - loss: 1.4429e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 3ms/step - loss: 1.5240e-04 - mean_absolute_error: 0.0092 - val_loss: 4.8068e-05 - val_mean_absolute_error: 0.0054
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 1.8489e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.3415e-04 - mean_absolute_error: 0.0085 53/126 [===========>..................] - ETA: 0s - loss: 1.5629e-04 - mean_absolute_error: 0.0092 79/126 [=================>............] - ETA: 0s - loss: 1.6271e-04 - mean_absolute_error: 0.0095102/126 [=======================>......] - ETA: 0s - loss: 1.5751e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5168e-04 - mean_absolute_error: 0.0093 - val_loss: 4.8905e-05 - val_mean_absolute_error: 0.0055
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 4.0824e-05 - mean_absolute_error: 0.0048 27/126 [=====>........................] - ETA: 0s - loss: 1.7570e-04 - mean_absolute_error: 0.0098 51/126 [===========>..................] - ETA: 0s - loss: 1.6779e-04 - mean_absolute_error: 0.0098 77/126 [=================>............] - ETA: 0s - loss: 1.6451e-04 - mean_absolute_error: 0.0096100/126 [======================>.......] - ETA: 0s - loss: 1.7570e-04 - mean_absolute_error: 0.0100126/126 [==============================] - ETA: 0s - loss: 1.7815e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 2ms/step - loss: 1.7815e-04 - mean_absolute_error: 0.0102 - val_loss: 6.5740e-05 - val_mean_absolute_error: 0.0066
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 1.0469e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.4125e-04 - mean_absolute_error: 0.0091 53/126 [===========>..................] - ETA: 0s - loss: 1.5380e-04 - mean_absolute_error: 0.0093 79/126 [=================>............] - ETA: 0s - loss: 1.4478e-04 - mean_absolute_error: 0.0091104/126 [=======================>......] - ETA: 0s - loss: 1.4710e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.6686e-04 - mean_absolute_error: 0.0097 - val_loss: 6.9068e-05 - val_mean_absolute_error: 0.0068
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 1.9485e-04 - mean_absolute_error: 0.0101 28/126 [=====>........................] - ETA: 0s - loss: 1.3492e-04 - mean_absolute_error: 0.0088 54/126 [===========>..................] - ETA: 0s - loss: 1.2715e-04 - mean_absolute_error: 0.0086 79/126 [=================>............] - ETA: 0s - loss: 1.2781e-04 - mean_absolute_error: 0.0087106/126 [========================>.....] - ETA: 0s - loss: 1.5601e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5585e-04 - mean_absolute_error: 0.0094 - val_loss: 4.7425e-05 - val_mean_absolute_error: 0.0053
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 1.3895e-04 - mean_absolute_error: 0.0094 27/126 [=====>........................] - ETA: 0s - loss: 1.3677e-04 - mean_absolute_error: 0.0086 53/126 [===========>..................] - ETA: 0s - loss: 1.4889e-04 - mean_absolute_error: 0.0089 78/126 [=================>............] - ETA: 0s - loss: 1.6163e-04 - mean_absolute_error: 0.0094103/126 [=======================>......] - ETA: 0s - loss: 1.5984e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.6275e-04 - mean_absolute_error: 0.0096 - val_loss: 8.0459e-05 - val_mean_absolute_error: 0.0074
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 2.1319e-04 - mean_absolute_error: 0.0114 27/126 [=====>........................] - ETA: 0s - loss: 1.7312e-04 - mean_absolute_error: 0.0094 54/126 [===========>..................] - ETA: 0s - loss: 1.7367e-04 - mean_absolute_error: 0.0096 79/126 [=================>............] - ETA: 0s - loss: 1.5807e-04 - mean_absolute_error: 0.0093106/126 [========================>.....] - ETA: 0s - loss: 1.5210e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4576e-04 - mean_absolute_error: 0.0091 - val_loss: 5.9861e-05 - val_mean_absolute_error: 0.0062
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 8.0149e-05 - mean_absolute_error: 0.0069 27/126 [=====>........................] - ETA: 0s - loss: 1.4641e-04 - mean_absolute_error: 0.0094 53/126 [===========>..................] - ETA: 0s - loss: 1.8490e-04 - mean_absolute_error: 0.0104 79/126 [=================>............] - ETA: 0s - loss: 1.9006e-04 - mean_absolute_error: 0.0104105/126 [========================>.....] - ETA: 0s - loss: 1.7657e-04 - mean_absolute_error: 0.0100126/126 [==============================] - 0s 2ms/step - loss: 1.6707e-04 - mean_absolute_error: 0.0098 - val_loss: 5.0929e-05 - val_mean_absolute_error: 0.0057
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 1.2228e-04 - mean_absolute_error: 0.0081 28/126 [=====>........................] - ETA: 0s - loss: 1.4778e-04 - mean_absolute_error: 0.0089 54/126 [===========>..................] - ETA: 0s - loss: 1.5436e-04 - mean_absolute_error: 0.0091 79/126 [=================>............] - ETA: 0s - loss: 1.5407e-04 - mean_absolute_error: 0.0092105/126 [========================>.....] - ETA: 0s - loss: 1.4586e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4535e-04 - mean_absolute_error: 0.0090 - val_loss: 4.9671e-05 - val_mean_absolute_error: 0.0056
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 1.2896e-04 - mean_absolute_error: 0.0086 28/126 [=====>........................] - ETA: 0s - loss: 1.3309e-04 - mean_absolute_error: 0.0091 54/126 [===========>..................] - ETA: 0s - loss: 1.4512e-04 - mean_absolute_error: 0.0093 79/126 [=================>............] - ETA: 0s - loss: 1.4777e-04 - mean_absolute_error: 0.0092104/126 [=======================>......] - ETA: 0s - loss: 1.5218e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.6579e-04 - mean_absolute_error: 0.0098 - val_loss: 1.0151e-04 - val_mean_absolute_error: 0.0084
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 2.1685e-04 - mean_absolute_error: 0.0119 27/126 [=====>........................] - ETA: 0s - loss: 2.7914e-04 - mean_absolute_error: 0.0130 53/126 [===========>..................] - ETA: 0s - loss: 2.1625e-04 - mean_absolute_error: 0.0111 79/126 [=================>............] - ETA: 0s - loss: 1.8987e-04 - mean_absolute_error: 0.0104105/126 [========================>.....] - ETA: 0s - loss: 1.7927e-04 - mean_absolute_error: 0.0100126/126 [==============================] - 0s 2ms/step - loss: 1.7198e-04 - mean_absolute_error: 0.0099 - val_loss: 9.3581e-05 - val_mean_absolute_error: 0.0080
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 1.3875e-04 - mean_absolute_error: 0.0097 27/126 [=====>........................] - ETA: 0s - loss: 1.6960e-04 - mean_absolute_error: 0.0097 52/126 [===========>..................] - ETA: 0s - loss: 1.5635e-04 - mean_absolute_error: 0.0093 77/126 [=================>............] - ETA: 0s - loss: 1.5092e-04 - mean_absolute_error: 0.0092103/126 [=======================>......] - ETA: 0s - loss: 1.5111e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4486e-04 - mean_absolute_error: 0.0090 - val_loss: 9.4382e-05 - val_mean_absolute_error: 0.0080
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 1.9167e-04 - mean_absolute_error: 0.0113 26/126 [=====>........................] - ETA: 0s - loss: 1.6596e-04 - mean_absolute_error: 0.0096 52/126 [===========>..................] - ETA: 0s - loss: 1.3569e-04 - mean_absolute_error: 0.0088 77/126 [=================>............] - ETA: 0s - loss: 1.5493e-04 - mean_absolute_error: 0.0093103/126 [=======================>......] - ETA: 0s - loss: 1.5178e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4683e-04 - mean_absolute_error: 0.0091 - val_loss: 5.4252e-05 - val_mean_absolute_error: 0.0058
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 1.4022e-04 - mean_absolute_error: 0.0086 27/126 [=====>........................] - ETA: 0s - loss: 2.0513e-04 - mean_absolute_error: 0.0104 52/126 [===========>..................] - ETA: 0s - loss: 1.9545e-04 - mean_absolute_error: 0.0105 78/126 [=================>............] - ETA: 0s - loss: 1.7436e-04 - mean_absolute_error: 0.0099104/126 [=======================>......] - ETA: 0s - loss: 1.7292e-04 - mean_absolute_error: 0.0100126/126 [==============================] - 0s 2ms/step - loss: 1.7853e-04 - mean_absolute_error: 0.0102 - val_loss: 1.5170e-04 - val_mean_absolute_error: 0.0106
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 3.5119e-04 - mean_absolute_error: 0.0154 27/126 [=====>........................] - ETA: 0s - loss: 1.8022e-04 - mean_absolute_error: 0.0106 53/126 [===========>..................] - ETA: 0s - loss: 1.6941e-04 - mean_absolute_error: 0.0102 77/126 [=================>............] - ETA: 0s - loss: 1.5463e-04 - mean_absolute_error: 0.0096101/126 [=======================>......] - ETA: 0s - loss: 1.4923e-04 - mean_absolute_error: 0.0094126/126 [==============================] - ETA: 0s - loss: 1.5400e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5400e-04 - mean_absolute_error: 0.0094 - val_loss: 5.1511e-05 - val_mean_absolute_error: 0.0057
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 1.1822e-04 - mean_absolute_error: 0.0088 26/126 [=====>........................] - ETA: 0s - loss: 1.7870e-04 - mean_absolute_error: 0.0104 52/126 [===========>..................] - ETA: 0s - loss: 1.6526e-04 - mean_absolute_error: 0.0099 78/126 [=================>............] - ETA: 0s - loss: 1.6083e-04 - mean_absolute_error: 0.0097103/126 [=======================>......] - ETA: 0s - loss: 1.7300e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.7333e-04 - mean_absolute_error: 0.0101 - val_loss: 1.0602e-04 - val_mean_absolute_error: 0.0086
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 1.6115e-04 - mean_absolute_error: 0.0109 27/126 [=====>........................] - ETA: 0s - loss: 1.8398e-04 - mean_absolute_error: 0.0101 50/126 [==========>...................] - ETA: 0s - loss: 1.7203e-04 - mean_absolute_error: 0.0101 75/126 [================>.............] - ETA: 0s - loss: 1.5810e-04 - mean_absolute_error: 0.0096 99/126 [======================>.......] - ETA: 0s - loss: 1.5979e-04 - mean_absolute_error: 0.0096124/126 [============================>.] - ETA: 0s - loss: 1.5669e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5628e-04 - mean_absolute_error: 0.0095 - val_loss: 9.0626e-05 - val_mean_absolute_error: 0.0079
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 1.8772e-04 - mean_absolute_error: 0.0091 27/126 [=====>........................] - ETA: 0s - loss: 1.6809e-04 - mean_absolute_error: 0.0096 52/126 [===========>..................] - ETA: 0s - loss: 1.9232e-04 - mean_absolute_error: 0.0106 77/126 [=================>............] - ETA: 0s - loss: 1.9624e-04 - mean_absolute_error: 0.0107103/126 [=======================>......] - ETA: 0s - loss: 1.9194e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 1.8544e-04 - mean_absolute_error: 0.0104 - val_loss: 5.3912e-05 - val_mean_absolute_error: 0.0058
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 1.9888e-04 - mean_absolute_error: 0.0098 27/126 [=====>........................] - ETA: 0s - loss: 1.5032e-04 - mean_absolute_error: 0.0090 52/126 [===========>..................] - ETA: 0s - loss: 1.4184e-04 - mean_absolute_error: 0.0089 77/126 [=================>............] - ETA: 0s - loss: 1.3544e-04 - mean_absolute_error: 0.0088103/126 [=======================>......] - ETA: 0s - loss: 1.3476e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.3613e-04 - mean_absolute_error: 0.0087 - val_loss: 7.7263e-05 - val_mean_absolute_error: 0.0072
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 8.7139e-05 - mean_absolute_error: 0.0074 27/126 [=====>........................] - ETA: 0s - loss: 1.3289e-04 - mean_absolute_error: 0.0089 52/126 [===========>..................] - ETA: 0s - loss: 1.3124e-04 - mean_absolute_error: 0.0088 77/126 [=================>............] - ETA: 0s - loss: 1.4599e-04 - mean_absolute_error: 0.0093102/126 [=======================>......] - ETA: 0s - loss: 1.3983e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4462e-04 - mean_absolute_error: 0.0090 - val_loss: 1.0925e-04 - val_mean_absolute_error: 0.0089
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 1.3310e-04 - mean_absolute_error: 0.0090 28/126 [=====>........................] - ETA: 0s - loss: 1.7533e-04 - mean_absolute_error: 0.0098 54/126 [===========>..................] - ETA: 0s - loss: 1.5608e-04 - mean_absolute_error: 0.0092 80/126 [==================>...........] - ETA: 0s - loss: 1.5694e-04 - mean_absolute_error: 0.0093105/126 [========================>.....] - ETA: 0s - loss: 1.5562e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5001e-04 - mean_absolute_error: 0.0092 - val_loss: 4.6451e-05 - val_mean_absolute_error: 0.0053
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 7.1986e-05 - mean_absolute_error: 0.0063 28/126 [=====>........................] - ETA: 0s - loss: 1.6194e-04 - mean_absolute_error: 0.0093 54/126 [===========>..................] - ETA: 0s - loss: 1.4792e-04 - mean_absolute_error: 0.0088 80/126 [==================>...........] - ETA: 0s - loss: 1.4427e-04 - mean_absolute_error: 0.0089103/126 [=======================>......] - ETA: 0s - loss: 1.4170e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.4679e-04 - mean_absolute_error: 0.0091 - val_loss: 7.7814e-05 - val_mean_absolute_error: 0.0073
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 9.3589e-05 - mean_absolute_error: 0.0081 27/126 [=====>........................] - ETA: 0s - loss: 1.2473e-04 - mean_absolute_error: 0.0085 52/126 [===========>..................] - ETA: 0s - loss: 1.4378e-04 - mean_absolute_error: 0.0091 78/126 [=================>............] - ETA: 0s - loss: 1.4338e-04 - mean_absolute_error: 0.0089103/126 [=======================>......] - ETA: 0s - loss: 1.4203e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.3785e-04 - mean_absolute_error: 0.0088 - val_loss: 5.4173e-05 - val_mean_absolute_error: 0.0059
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 8.1734e-05 - mean_absolute_error: 0.0073 25/126 [====>.........................] - ETA: 0s - loss: 1.3522e-04 - mean_absolute_error: 0.0086 48/126 [==========>...................] - ETA: 0s - loss: 1.2513e-04 - mean_absolute_error: 0.0084 74/126 [================>.............] - ETA: 0s - loss: 1.4616e-04 - mean_absolute_error: 0.0089100/126 [======================>.......] - ETA: 0s - loss: 1.4684e-04 - mean_absolute_error: 0.0091126/126 [==============================] - ETA: 0s - loss: 1.4447e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4447e-04 - mean_absolute_error: 0.0090 - val_loss: 6.7188e-05 - val_mean_absolute_error: 0.0067
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 1.4573e-04 - mean_absolute_error: 0.0098 27/126 [=====>........................] - ETA: 0s - loss: 1.4299e-04 - mean_absolute_error: 0.0088 53/126 [===========>..................] - ETA: 0s - loss: 1.3845e-04 - mean_absolute_error: 0.0088 78/126 [=================>............] - ETA: 0s - loss: 1.4774e-04 - mean_absolute_error: 0.0090104/126 [=======================>......] - ETA: 0s - loss: 1.4344e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.4168e-04 - mean_absolute_error: 0.0089 - val_loss: 5.6941e-05 - val_mean_absolute_error: 0.0060
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 7.7086e-05 - mean_absolute_error: 0.0071 26/126 [=====>........................] - ETA: 0s - loss: 1.2342e-04 - mean_absolute_error: 0.0086 51/126 [===========>..................] - ETA: 0s - loss: 1.1792e-04 - mean_absolute_error: 0.0083 77/126 [=================>............] - ETA: 0s - loss: 1.4546e-04 - mean_absolute_error: 0.0092102/126 [=======================>......] - ETA: 0s - loss: 1.8378e-04 - mean_absolute_error: 0.0104126/126 [==============================] - 0s 2ms/step - loss: 1.8454e-04 - mean_absolute_error: 0.0103 - val_loss: 8.7996e-05 - val_mean_absolute_error: 0.0078
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 1.6170e-04 - mean_absolute_error: 0.0104 27/126 [=====>........................] - ETA: 0s - loss: 2.3524e-04 - mean_absolute_error: 0.0118 53/126 [===========>..................] - ETA: 0s - loss: 1.9319e-04 - mean_absolute_error: 0.0106 78/126 [=================>............] - ETA: 0s - loss: 1.8478e-04 - mean_absolute_error: 0.0105103/126 [=======================>......] - ETA: 0s - loss: 1.9558e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 1.9924e-04 - mean_absolute_error: 0.0110 - val_loss: 9.1676e-05 - val_mean_absolute_error: 0.0080
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 1.1681e-04 - mean_absolute_error: 0.0086 28/126 [=====>........................] - ETA: 0s - loss: 1.9252e-04 - mean_absolute_error: 0.0109 55/126 [============>.................] - ETA: 0s - loss: 1.6690e-04 - mean_absolute_error: 0.0101 80/126 [==================>...........] - ETA: 0s - loss: 1.5145e-04 - mean_absolute_error: 0.0095106/126 [========================>.....] - ETA: 0s - loss: 1.5693e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5495e-04 - mean_absolute_error: 0.0094 - val_loss: 6.9551e-05 - val_mean_absolute_error: 0.0068
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 1.0978e-04 - mean_absolute_error: 0.0091 28/126 [=====>........................] - ETA: 0s - loss: 1.1943e-04 - mean_absolute_error: 0.0087 54/126 [===========>..................] - ETA: 0s - loss: 1.4182e-04 - mean_absolute_error: 0.0088 79/126 [=================>............] - ETA: 0s - loss: 1.3678e-04 - mean_absolute_error: 0.0087105/126 [========================>.....] - ETA: 0s - loss: 1.3311e-04 - mean_absolute_error: 0.0086126/126 [==============================] - 0s 2ms/step - loss: 1.2963e-04 - mean_absolute_error: 0.0085 - val_loss: 7.1704e-05 - val_mean_absolute_error: 0.0069
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 1.6865e-04 - mean_absolute_error: 0.0109 27/126 [=====>........................] - ETA: 0s - loss: 1.4720e-04 - mean_absolute_error: 0.0092 53/126 [===========>..................] - ETA: 0s - loss: 1.4977e-04 - mean_absolute_error: 0.0092 78/126 [=================>............] - ETA: 0s - loss: 1.3973e-04 - mean_absolute_error: 0.0089104/126 [=======================>......] - ETA: 0s - loss: 1.4461e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4414e-04 - mean_absolute_error: 0.0090 - val_loss: 5.0718e-05 - val_mean_absolute_error: 0.0057
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 8.2006e-05 - mean_absolute_error: 0.0074 27/126 [=====>........................] - ETA: 0s - loss: 1.5095e-04 - mean_absolute_error: 0.0095 53/126 [===========>..................] - ETA: 0s - loss: 1.6620e-04 - mean_absolute_error: 0.0097 79/126 [=================>............] - ETA: 0s - loss: 1.5729e-04 - mean_absolute_error: 0.0095105/126 [========================>.....] - ETA: 0s - loss: 1.5623e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5586e-04 - mean_absolute_error: 0.0095 - val_loss: 4.4641e-05 - val_mean_absolute_error: 0.0051
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 9.7220e-05 - mean_absolute_error: 0.0083 27/126 [=====>........................] - ETA: 0s - loss: 1.4909e-04 - mean_absolute_error: 0.0095 54/126 [===========>..................] - ETA: 0s - loss: 1.4270e-04 - mean_absolute_error: 0.0092 80/126 [==================>...........] - ETA: 0s - loss: 1.3423e-04 - mean_absolute_error: 0.0089106/126 [========================>.....] - ETA: 0s - loss: 1.3441e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.3653e-04 - mean_absolute_error: 0.0088 - val_loss: 4.8346e-05 - val_mean_absolute_error: 0.0055
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 7.5780e-05 - mean_absolute_error: 0.0063 28/126 [=====>........................] - ETA: 0s - loss: 1.2610e-04 - mean_absolute_error: 0.0084 53/126 [===========>..................] - ETA: 0s - loss: 1.4574e-04 - mean_absolute_error: 0.0087 78/126 [=================>............] - ETA: 0s - loss: 1.3712e-04 - mean_absolute_error: 0.0086103/126 [=======================>......] - ETA: 0s - loss: 1.3248e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 2ms/step - loss: 1.3383e-04 - mean_absolute_error: 0.0086 - val_loss: 8.9255e-05 - val_mean_absolute_error: 0.0079
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 1.8530e-04 - mean_absolute_error: 0.0120 28/126 [=====>........................] - ETA: 0s - loss: 2.0174e-04 - mean_absolute_error: 0.0109 55/126 [============>.................] - ETA: 0s - loss: 1.9057e-04 - mean_absolute_error: 0.0105 81/126 [==================>...........] - ETA: 0s - loss: 1.8004e-04 - mean_absolute_error: 0.0103107/126 [========================>.....] - ETA: 0s - loss: 1.7429e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.7606e-04 - mean_absolute_error: 0.0102 - val_loss: 5.6107e-05 - val_mean_absolute_error: 0.0060
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 1.5862e-04 - mean_absolute_error: 0.0102 27/126 [=====>........................] - ETA: 0s - loss: 1.3127e-04 - mean_absolute_error: 0.0088 53/126 [===========>..................] - ETA: 0s - loss: 1.3180e-04 - mean_absolute_error: 0.0085 79/126 [=================>............] - ETA: 0s - loss: 1.2967e-04 - mean_absolute_error: 0.0085104/126 [=======================>......] - ETA: 0s - loss: 1.3019e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 2ms/step - loss: 1.2753e-04 - mean_absolute_error: 0.0084 - val_loss: 5.3433e-05 - val_mean_absolute_error: 0.0058
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 7.9802e-05 - mean_absolute_error: 0.0076 27/126 [=====>........................] - ETA: 0s - loss: 1.3619e-04 - mean_absolute_error: 0.0086 52/126 [===========>..................] - ETA: 0s - loss: 1.2378e-04 - mean_absolute_error: 0.0084 78/126 [=================>............] - ETA: 0s - loss: 1.2457e-04 - mean_absolute_error: 0.0084103/126 [=======================>......] - ETA: 0s - loss: 1.4301e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4582e-04 - mean_absolute_error: 0.0090 - val_loss: 1.1673e-04 - val_mean_absolute_error: 0.0092
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 1.7894e-04 - mean_absolute_error: 0.0104 27/126 [=====>........................] - ETA: 0s - loss: 1.7602e-04 - mean_absolute_error: 0.0094 53/126 [===========>..................] - ETA: 0s - loss: 1.4764e-04 - mean_absolute_error: 0.0088 78/126 [=================>............] - ETA: 0s - loss: 1.3925e-04 - mean_absolute_error: 0.0086103/126 [=======================>......] - ETA: 0s - loss: 1.3615e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 2ms/step - loss: 1.3487e-04 - mean_absolute_error: 0.0087 - val_loss: 7.3615e-05 - val_mean_absolute_error: 0.0070
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 1.3718e-04 - mean_absolute_error: 0.0092 28/126 [=====>........................] - ETA: 0s - loss: 1.1387e-04 - mean_absolute_error: 0.0077 54/126 [===========>..................] - ETA: 0s - loss: 1.1840e-04 - mean_absolute_error: 0.0080 79/126 [=================>............] - ETA: 0s - loss: 1.2187e-04 - mean_absolute_error: 0.0082104/126 [=======================>......] - ETA: 0s - loss: 1.1916e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.2500e-04 - mean_absolute_error: 0.0083 - val_loss: 5.0637e-05 - val_mean_absolute_error: 0.0056
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 9.3886e-05 - mean_absolute_error: 0.0083 27/126 [=====>........................] - ETA: 0s - loss: 1.2614e-04 - mean_absolute_error: 0.0088 53/126 [===========>..................] - ETA: 0s - loss: 1.2114e-04 - mean_absolute_error: 0.0085 78/126 [=================>............] - ETA: 0s - loss: 1.1883e-04 - mean_absolute_error: 0.0084104/126 [=======================>......] - ETA: 0s - loss: 1.2476e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.2848e-04 - mean_absolute_error: 0.0085 - val_loss: 1.2184e-04 - val_mean_absolute_error: 0.0095
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 1.6728e-04 - mean_absolute_error: 0.0100 27/126 [=====>........................] - ETA: 0s - loss: 1.1755e-04 - mean_absolute_error: 0.0082 52/126 [===========>..................] - ETA: 0s - loss: 1.4525e-04 - mean_absolute_error: 0.0088 78/126 [=================>............] - ETA: 0s - loss: 1.4874e-04 - mean_absolute_error: 0.0090103/126 [=======================>......] - ETA: 0s - loss: 1.3905e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 2ms/step - loss: 1.3486e-04 - mean_absolute_error: 0.0087 - val_loss: 4.4510e-05 - val_mean_absolute_error: 0.0052
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 8.4307e-05 - mean_absolute_error: 0.0070 27/126 [=====>........................] - ETA: 0s - loss: 1.2685e-04 - mean_absolute_error: 0.0086 52/126 [===========>..................] - ETA: 0s - loss: 1.6904e-04 - mean_absolute_error: 0.0101 77/126 [=================>............] - ETA: 0s - loss: 1.7338e-04 - mean_absolute_error: 0.0102103/126 [=======================>......] - ETA: 0s - loss: 1.7024e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.6731e-04 - mean_absolute_error: 0.0099 - val_loss: 7.2397e-05 - val_mean_absolute_error: 0.0070
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 2.1926e-04 - mean_absolute_error: 0.0113 27/126 [=====>........................] - ETA: 0s - loss: 1.9663e-04 - mean_absolute_error: 0.0104 51/126 [===========>..................] - ETA: 0s - loss: 1.6393e-04 - mean_absolute_error: 0.0096 76/126 [=================>............] - ETA: 0s - loss: 1.5933e-04 - mean_absolute_error: 0.0096101/126 [=======================>......] - ETA: 0s - loss: 1.5607e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5826e-04 - mean_absolute_error: 0.0095 - val_loss: 4.3184e-05 - val_mean_absolute_error: 0.0050
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 1.1686e-04 - mean_absolute_error: 0.0085 27/126 [=====>........................] - ETA: 0s - loss: 1.7494e-04 - mean_absolute_error: 0.0104 53/126 [===========>..................] - ETA: 0s - loss: 1.7401e-04 - mean_absolute_error: 0.0100 78/126 [=================>............] - ETA: 0s - loss: 1.7834e-04 - mean_absolute_error: 0.0102103/126 [=======================>......] - ETA: 0s - loss: 1.6786e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6302e-04 - mean_absolute_error: 0.0097 - val_loss: 1.3563e-04 - val_mean_absolute_error: 0.0101
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 1.6468e-04 - mean_absolute_error: 0.0105 27/126 [=====>........................] - ETA: 0s - loss: 1.6549e-04 - mean_absolute_error: 0.0101 52/126 [===========>..................] - ETA: 0s - loss: 1.7092e-04 - mean_absolute_error: 0.0103 77/126 [=================>............] - ETA: 0s - loss: 1.6283e-04 - mean_absolute_error: 0.0098101/126 [=======================>......] - ETA: 0s - loss: 1.5688e-04 - mean_absolute_error: 0.0094125/126 [============================>.] - ETA: 0s - loss: 1.4500e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4498e-04 - mean_absolute_error: 0.0091 - val_loss: 4.2778e-05 - val_mean_absolute_error: 0.0049

<keras.src.callbacks.History at 0x267df8c37d0>

After the training and fitting of the Machine Learning model for Switzerland, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Switzerland with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
switzerland_train_pred = switzerland_model.predict(X_switzerland_train).flatten()

plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4)
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Training Set Predictions vs. Observations")
plt.show()

switzerland_val_pred = switzerland_model.predict(X_switzerland_val).flatten()

plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Validation Set Predictions vs. Observations")
plt.show()

switzerland_test_pred = switzerland_model.predict(X_switzerland_test).flatten()

plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 42s 47/126 [==========>...................] - ETA: 0s  97/126 [======================>.......] - ETA: 0s126/126 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Switzerland’s international currency rate with the United States.

```{python}
# Plotting Switzerland's observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model) 
plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4)
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Switzerland's Dataset - Predictions vs. Observations")
plt.show()
```

To put it all together, I made one large visualization to encompass all 4 countries’ Machine Learning model prediction-based vs. observation-based visualizations - Austrailia, Canada, the United Kingdom, and Switzerland as shown below.

```{python}
# Plotting all countries' observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model in an overall sense) 
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1, linestyle="solid")

plt.plot(dates_canada_train, canada_train_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_train, y_canada_train, linewidth=1, linestyle="dashed")
plt.plot(dates_canada_val, canada_val_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_val, y_canada_val, linewidth=1, linestyle="dashed")
plt.plot(dates_canada_test, canada_test_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_test, y_canada_test, linewidth=1, linestyle="dashed")

plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1, linestyle="dotted")
plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1, linestyle="dotted")
plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1, linestyle="dotted")

plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1, linestyle="dashdot")
plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1, linestyle="dashdot")
plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1, linestyle="dashdot")

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of All Four Countries' Datasets - Predictions vs. Observations")
plt.show()
```

Ultimately, here is the moment of truth: I tried to recursively predict Austrailia’s future expected projection on its international currency rate between the United States through using my LSTM prediction model as shown below. By using the data from the training dataset for Austrailia, I tried to contrast the effectiveness of the model by constrasting its projection to the actual validation and testing data results. However, unfortunately, it did not predict as well as I hoped, but this was a good experiment, nonetheless.

```{python}
# Attempt at using the model to predict into the future for Austrailia's currency 
# rate
recursive_pred: list = []
recursive_dates = np.concatenate([dates_austrailia_val, dates_austrailia_test])

for target_date in recursive_dates:
    last_window = deepcopy(X_austrailia_train[-1])
    next_pred = austrailia_model.predict(np.array([last_window])).flatten()
    recursive_pred.append(next_pred)
    last_window[-1] = next_pred
```

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To visualize the results of my recursive prediction that I completed, I added these results to the previous large visualization that encompassed all 4 countries’ Machine Learning model prediction-based vs. observation-based visualizations - Austrailia, Canada, the United Kingdom, and Switzerland as shown below.

```{python}
# Plotting all countries' observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model in an overall sense) along with the new recursive prediction
# results
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1, linestyle="solid")
plt.plot(recursive_dates, recursive_pred, linewidth=4, linestyle="solid")

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations",
            "Recursive Predictions"], loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations (With Recursive Pred. Attempt)")
plt.show()
```

Conclusions

• Returning to my attempt to test the ability for my Machine Learning model to predict the International Currency rates for Austrailia from just the range of my training dataset as shown above, I felt that its prediction was acceptable in my opinion giving the averaging across the currency rate values in my validation and testing datasets would have proven to be considerably close in its precision.

• Thus, I reached the conclusion that my International Currency Predictor would not be a viable Machine Learning model for any real-life applications. This Machine Learning model raised a valuable point - financial market prediction is a difficult endeavor to accurately predict. From a more general point of view, this International Currency Maching Learning prediction model is not totally inaccurate or untrustworthy but rather it is only on the micro-scale. These financial predictions do not always account for current events in that respective country such as changing domestic or international relations or constantly changing socioeconomic trends which could significantly sway market confidence and impact currency rate fluctuation. Consequently, economists often have to look at larger economic trends across several decades in order to make more educated predictions.

• Ultimately, I learned a great deal about the complexities of financial prediction and usage of Machine Learning models as an attempt to solve this issue. Even though my model was not as successful at predicting as I would have hoped, I am interested to see the future of Machine Learning as it soon dominates these economic sub-field and continues to rise in its relevance by being a pivotal talking point for the future and applied soon enough to other sectors of our society.

Reference Sources and Citations (IEEE Format)

To complete this blog post, I used the following online sources as references for developing this:

[1] US International Exchange Rates Dataset:

• B. Ferreira, “Foreign Exchange Rates 2000-2019”, 2019. [Online]. Available: https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019. [Accessed: 04-Sep.-2023].

[2] Tutorial on Basics of LSTM and Prediction & Forecasting in Machine Learning:

• G. Hogg, “Stock Price Prediction & Forecasting with LSTM Neural Networks in Python”, YouTube, 26-Mar.-2022. [Online.] Available: https://www.youtube.com/watch?v=CbTU92pbDKw. [Accessed: 06-Sep.-2023].

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